Loudness model anti-failure method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a loudness model anti-failure method and device, electronic equipment and a storage medium. The method comprises the following steps: determining different reference volume attribute information when a target video is played in different playing stages, each playing stage being different playing amount intervals divided according to different playing amounts reached in sequence when the target video is played, each piece of reference volume attribute information is used for describing volume loudness adjustment operation performed on the target video under the condition that the target video starts to be played at different reference volume loudnesses in each playing stage; and adjusting a volume loudness balance parameter of the target loudness model according to different reference volume attribute information so as to suppress performance degradation of the target loudness model in volume loudness balance. According to the scheme, the change of the video under the volume loudness balancing strategy is analyzed through the volume attributes updated by different data magnitudes, the effect of the volume loudness balancing strategy is monitored, and whether the volume loudness balancing strategy fails or not is judged in time.

Description

Technical Field

[0001] This disclosure relates to audio processing technology, and more particularly to a loudness model failure prevention method, apparatus, electronic device, and storage medium. Background Technology

[0002] In the field of audio processing on the playback end, loudness equalization technology is of key significance for improving playback effect. Its core value lies in its ability to effectively solve the problem of overall loudness uniformity on the playback end.

[0003] Currently, various methods are widely used in loudness equalization solutions, including adaptive loudness schemes based on Bayesian probability, personalized loudness equalization schemes for short videos trained with multimodal models, and video volume adjustment prediction schemes for optimizing online loudness equalization algorithms. However, as these methods are applied more extensively, some problems have gradually emerged. For example, there is still room for further reduction in online volume adjustment rates. From the perspective of the playback end, different playback ends typically have different volume preferences; some playback ends are extremely sensitive to loudness changes, while others are relatively insensitive. Moreover, from the perspective of video content, different audio types have significantly different requirements for loudness equalization. For music content, excessive pursuit of loudness equalization may destroy its original dynamic effects. Although loudness equalization solutions set multiple algorithm inputs for parameter adjustment, it is still difficult to ensure optimal playback effects when watching every video in the long run. Furthermore, loudness equalization solutions may gradually become ineffective over time, leading to a series of problems. Summary of the Invention

[0004] This disclosure provides a loudness model failure prevention method, apparatus, electronic device, and storage medium to analyze the changes in video under a volume loudness equalization strategy by updating volume attributes at different data levels, thereby monitoring the effect of the volume loudness equalization strategy and promptly determining whether the volume loudness equalization strategy has failed.

[0005] In a first aspect, embodiments of this disclosure provide a loudness model failure prevention method, the method comprising:

[0006] The reference volume attribute information of the target video is determined when the target video is played in different playback stages. The target video is a video that is processed by volume loudness equalization using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volume reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different reference volume loudness in each playback stage.

[0007] Based on different reference volume attribute information, the volume loudness equalization parameters of the target loudness model are adjusted to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0008] Secondly, embodiments of this disclosure also provide a loudness model failure prevention device, the device comprising:

[0009] The determination module is used to determine different reference volume attribute information of the target video when it is played in different playback stages. The target video is a video that has undergone volume loudness equalization processing using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volumes reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different reference volume loudness in each playback stage.

[0010] The adjustment module is used to adjust the volume loudness equalization parameters of the target loudness model according to different reference volume attribute information, so as to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0011] Thirdly, this disclosure also provides an electronic device, the electronic device comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the loudness model anti-failure method described in any of the above embodiments.

[0015] Fourthly, this disclosure also provides a computer-readable medium storing computer instructions that, when executed by a processor, implement the loudness model anti-failure method described in any of the above embodiments.

[0016] In this embodiment, during the playback of a target video, the diversity of the audience and the complexity of the viewing environment change as the number of plays increases. By dividing the playback into stages based on different play counts and determining the reference volume attribute information for each stage, the audio performance differences of the video under different propagation levels can be accurately captured. Each reference volume attribute information details the volume loudness adjustment operation of the target video when starting playback from different baseline volume levels, providing in-depth understanding of the volume adjustment of the target video under different initial volume settings. Moreover, the reference volume attribute information is based on actual playback data collection, accurately reflecting the volume loudness adjustment requirements of the target video at each playback stage. Thus, during the volume loudness equalization process, the target loudness model may degrade due to various factors, such as changes in audio content, audience, and playback environment. By continuously adjusting the volume loudness equalization parameters of the target loudness model according to the reference volume attribute information at different playback stages, problems that may cause the volume loudness equalization strategy to fail can be promptly identified and resolved. This avoids the problem of the volume loudness equalization strategy failing due to its inability to adapt to changes throughout the entire playback cycle, ensuring that the video provides a good volume loudness effect at each playback stage.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0018] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0019] Figure 1 This is a schematic flowchart of a loudness model failure prevention method provided in an embodiment of this disclosure;

[0020] Figure 2 This is a schematic diagram of volume attributes at different stages of video playback provided in an embodiment of this disclosure;

[0021] Figure 3 This is a schematic diagram illustrating a loudness model failure prevention method during video playback provided in an embodiment of this disclosure;

[0022] Figure 4 This is a schematic diagram illustrating another method for preventing loudness model failure during video playback, provided in an embodiment of this disclosure.

[0023] Figure 5This is a schematic diagram of another loudness model failure prevention method provided in this embodiment of the disclosure;

[0024] Figure 6 This is a schematic diagram of a loudness model anti-failure device provided in an embodiment of this disclosure;

[0025] Figure 7 This is a schematic diagram of the structure of an electronic device that implements a loudness model failure prevention method according to an embodiment of this disclosure. Detailed Implementation

[0026] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0027] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0028] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0029] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0030] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0031] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0032] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0033] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0034] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0035] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0036] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0037] Figure 1 This is a flowchart illustrating a loudness model failure prevention method provided in an embodiment of this disclosure. This embodiment is applicable to monitoring the volume loudness equalization effect of the volume loudness equalization strategy used in video playback to prevent the volume loudness effect from gradually deteriorating due to the failure of the volume loudness equalization strategy. This loudness model failure prevention method can be executed by a loudness model failure prevention device, which can be implemented in the form of software and / or hardware, and is generally integrated into any electronic device with network communication function, such as a mobile terminal, PC, or server.

[0038] like Figure 1 As shown, the loudness model failure prevention method of this disclosure embodiment may include the following process:

[0039] S110. Determine the different reference volume attribute information of the target video when it is played in different playback stages. The target video is a video that has undergone volume loudness equalization processing using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volumes reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different baseline volume loudness in each playback stage.

[0040] The target video is the object of attention and processing in the evaluation of volume loudness equalization. During playback, the target video uses a target loudness model for volume loudness equalization. The loudness model can predict or calculate the volume loudness of the video based on its characteristics. During video processing, such as mixing and playback, it is used to ensure that the volume loudness of the video during playback remains balanced within a preset volume loudness range. In other words, the loudness model can standardize the volume loudness of the video to keep the volume loudness of different videos relatively consistent during playback, ensuring that the volume loudness of the video during playback is in a suitable and balanced state, avoiding situations where some parts are too loud or too quiet, which would affect the viewing experience.

[0041] As time progresses, the target video will be played repeatedly, going through different playback stages during its lifecycle. These stages are determined by the number of views the video reaches during playback; different view count intervals represent different playback stages. See also... Figure 2 You can set the first playback stage to 0-10000 views of the target video, the second playback stage to 10001-20000 views, and so on.

[0042] For each playback stage of the target video, there is corresponding reference volume attribute information. This volume attribute information primarily describes the volume adjustment operations performed on the video at each playback stage when the target video starts playing at different reference volume levels. The reference volume level can be understood as the initial volume setting during video playback. For example, in a playback stage, starting playback at 50% of the reference volume might make the target video sound too quiet, leading to an increase in volume; conversely, starting at 70% might make the target video sound too loud, leading to a decrease in volume. These volume adjustment operations constitute the volume attribute information for that playback stage at that reference volume level.

[0043] The segmentation of playback stages allows for specialized analysis of videos at different levels of dissemination. Within each playback stage, volume adjustment information based on different baseline volume levels reveals the specific volume requirements for that stage. For example, for an educational video, in the initial stages with low viewership, the primary audience might be students studying in quiet environments, requiring high clarity in the explanations and thus desiring a lower baseline volume with smaller adjustments. As viewership increases, more people watch in diverse environments, such as office workers studying in noisy settings. In this case, adjustments based on the new playback stage's volume attributes are necessary, potentially requiring an increase in baseline volume or optimization of the loudness model to accommodate a wider range of usage scenarios.

[0044] The method of segmenting playback stages to collect volume attribute information takes into account that as the number of views of a target video increases, factors such as the audience and viewing environment may change, which may affect the required volume. This segmented statistics are mainly to observe whether the behavior of adjusting the video volume changes as the number of views increases. This will indicate the impact of certain factors in the video content (such as audio style, plot development, etc.) or audience characteristics (such as attracting different types of viewers at different stages) on volume adjustment. For example, considering the changes in the diversity of the audience and the complexity of the viewing environment as the number of views increases, this method can adapt well to these changes. Different viewers have different listening habits, devices used, and viewing environments. As the video's reach expands and new viewers join, volume adjustment behavior will be recorded in the volume attribute information of the corresponding playback stage. For example, a music video may initially be watched by music lovers in a headphone environment, but as the number of views increases, it may be watched in a car stereo or home theater environment.

[0045] As an optional but non-limiting implementation, determining different reference volume attribute information for the target video during different playback stages includes the following steps A1-A2:

[0046] Step A1: Determine the first reference volume attribute information of the target video in the first playback stage. The first playback stage is the playback stage corresponding to the target video from the first playback until the number of plays of the target video reaches the first number of plays. The first reference volume attribute information is used to describe the number of times the target video is reduced in volume and the number of times the volume is increased when the target video starts playing at different reference volume levels in the first playback stage.

[0047] Step A2: Determine the second reference volume attribute information of the target video in the second playback stage. The second playback stage is the playback stage corresponding to when the target video reaches the first playback volume and when the target video reaches the second playback volume. The second reference volume attribute information is used to describe the number of times the target video is reduced in volume and the number of times the target video is increased in volume when it starts playing in the second playback stage with different baseline volume levels. The first playback volume is less than the second playback volume.

[0048] See Figure 2 The first playback phase can be defined as the period from when the target video is first played until it reaches the first playback count. The first playback phase primarily focuses on the volume adjustment using a loudness model for volume equalization during the initial dissemination of the target video. For example, for a newly released funny short video on a video platform, the first playback phase might be the period from its release until it reaches 10,000 views. At this stage, the viewers are likely mostly the creator's fans or those who benefited from early platform recommendations.

[0049] See Figure 2 The first reference volume attribute information pertains to the playback of the target video during the first playback phase. It details the number of times the target video's volume was decreased and increased during this phase, when played at different reference volume levels (e.g., 30%, 50%, 70%). This information is obtained by collecting volume adjustment data during this phase. For example, when played at 50% reference volume, statistics show 20 instances of volume being decreased and 30 instances of volume being increased.

[0050] See Figure 2 The second playback stage can begin when the target video reaches the first playback count and continues until it reaches the second playback count. This second playback stage is the subsequent phase in the video's spread, signifying wider dissemination. This phased statistical approach allows observation of whether video volume adjustment behavior changes as playback counts increase. Continuing with the example of the funny short video, the second playback stage might be between 10,000 and 20,000 views, at which point more views may be attracted by the platform's recommendation algorithm.

[0051] See Figure 2The second reference volume attribute information mainly pertains to the playback of the target video during the second playback phase. Its meaning is similar to the second reference volume attribute information, but it focuses on the situation during the second playback phase. During the second playback phase, the volume adjustments of the target video at different baseline volume levels are also recorded. However, as the video playback volume increases, the specific adjustments to the target video's volume may change, and these adjustments may differ from those in the first playback phase. For example, during the second playback phase, when playing the target video at 50% of the baseline volume, the volume might be decreased 15 times and increased 40 times.

[0052] The first and second views are key metrics for defining playback stages; they represent the specific numerical values ​​indicating the playback stage of the target video. The first view count is less than the second view count, and their values ​​depend on the specific application scenario and video type. For example, for a niche interest video, the first view count might be 500, and the second view count 2000; while for a mainstream entertainment video, the first view count might be 10000, and the second view count 20000.

[0053] See Figure 2 By dividing the video into first and second playback stages, we can more effectively analyze the volume levels of a target video at different stages of its dissemination. In the initial stages of video release (first playback stage), viewers may share certain similarities, such as having a specific interest in the video's theme or being loyal fans of the creator. Their volume adjustment habits may reflect the audio and video needs of this core audience. However, as the video's views increase and it enters the second playback stage, a new audience joins. By analyzing the second reference volume attribute information, we can understand the new audience's expectations for volume loudness. This phased analysis is more adaptable to changes in the audience group than a uniform approach.

[0054] See Figure 2 The first and second reference volume attribute information provide clear data support for subsequent volume loudness adjustments. Based on this volume attribute information at different playback stages, we can understand the actual operation of the video at different playback stages and different reference volume loudnesses. For example, if the volume is turned down frequently during the first playback stage at a certain reference volume loudness, it indicates that the reference volume loudness may be too high at this stage, requiring corresponding adjustments to the loudness model. Similarly, if a new volume adjustment trend emerges during the second playback stage, the loudness model can be optimized accordingly to better meet the playback needs of the target video at different playback stages and increase the video's popularity when played.

[0055] For example, see Figure 2 The volume attribute corresponding to volume adjustment is generated in stages based on the number of views of the target video. The first reference volume attribute information is S (watch users > 10000), which is calculated and generated when the number of views of the target video first exceeds 10,000. Based on this, a second reference volume attribute information S' (watch users > 20000) is proposed, which is calculated and generated when the number of views of the target video first exceeds 20,000. In this way, each newly produced and released target video vid will have two volume attributes, one is S_1w and the other is S_2w.

[0056] The phased statistical approach described above effectively adapts to changes during video distribution, especially as video views increase, potentially leading to playback on different devices and viewing environments. The volume attributes of the first and second playback phases reflect the impact of these changes on volume loudness. For example, in the first playback phase, viewers might be more likely to watch on their phones in quiet indoor environments, while in the second phase, more viewers might be watching on tablets in in-car environments or noisy public places. By analyzing the volume attributes of both phases, the loudness model can be adjusted based on these changes, ensuring appropriate volume loudness across various playback scenarios.

[0057] S120. Adjust the volume loudness equalization parameters of the target loudness model according to different reference volume attribute information in order to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0058] See Figure 2 The different reference volume attribute information is a data set collected based on volume adjustment data during different playback stages of the target video (such as the first playback stage, the second playback stage, etc. mentioned earlier), played at different reference volume levels. This reference volume attribute information reflects the frequency of volume adjustments to the target video under various conditions. For example, in a certain playback stage, when the reference volume is 50%, there are more instances where the volume of the target video is increased, while when the reference volume is 70%, there are more instances where the volume of the target video is decreased. These volume adjustment data under different conditions constitute the reference volume attribute information, and the reference volume attribute information is unique for the same target video in each playback stage.

[0059] Reference volume attribute information can provide strong support for optimizing the target loudness model. This information can help identify potential problems with the loudness model at different playback stages. For example, if the volume of a specific type of audio (such as narration) is significantly adjusted during a playback stage, it may indicate that the loudness model is inadequate in handling that type of audio volume at that particular playback stage. Based on this feedback, the parameters of the loudness model can be adjusted to suppress performance degradation caused by changes in the audience and environment, extend the effective lifespan of the loudness model, and improve the stability of audio quality during video playback.

[0060] See Figure 2 and Figure 3 The volume loudness equalization parameters used in the target loudness model are key settings within the model, controlling the process of adjusting and balancing volume loudness. For example, these parameters might include gain values ​​for different audio frequency bands, audio compression ratios, and volume thresholds. Adjusting these parameters changes how the model processes volume loudness. During volume loudness equalization, the target loudness model may experience performance degradation due to various factors (such as changes in video content and playback environment). This manifests as a deterioration in the volume loudness equalization effect; for example, the previously balanced volume may become unstable, and the loudness of certain video segments may not meet expectations, leading to a decrease in the popularity of the target video when played.

[0061] See Figure 2 and Figure 3 Adjusting the volume loudness equalization parameters of the target loudness model based on different reference volume attribute information means updating the relevant volume loudness equalization parameters in the target loudness model by parsing the volume adjustment operation data of the target video collected at each playback stage. For example, if the reference volume attribute information shows that the volume loudness of the target video is frequently increased in a playback stage, it may be necessary to adjust the volume loudness equalization parameters of the loudness model to appropriately increase the initial loudness of that playback stage or adjust the audio gain parameters so that the loudness model can better adapt to the playback requirements of the target video in this playback stage, thereby suppressing performance degradation.

[0062] The volume loudness equalization parameters are a series of parameters used within the target loudness model to control volume loudness adjustment. These parameters determine how the loudness model performs audio loudness equalization processing on the input video and audio, including but not limited to gain for different frequencies, audio compression ratio, and dynamic range control of loudness. Different parameter settings will produce different volume loudness equalization effects.

[0063] By adjusting the volume loudness equalization parameters based on reference volume attribute information, the processing effect of the target loudness model can be dynamically optimized. The loudness model can adapt in real time to changes in the video playback stage, playback environment, and volume adjustment operations affecting the target video's volume. For example, in the initial stage of video playback, the playback requirements of the target video are relatively fixed. After adjusting the parameters based on the initial reference volume attribute information, the model can achieve good loudness equalization. As the video enters a wider dissemination stage, the playback requirements and playback environment of the target video will change. Only by adjusting the parameters again based on the new reference volume attribute information can the volume loudness balance be continuously maintained, ensuring that the video and audio remain of high quality. Furthermore, this approach can suppress performance degradation of the target loudness model, preventing frequent adjustments due to volume issues. This avoids situations where the sound is too low to hear clearly or too high to cause discomfort, achieving appropriate volume loudness whether watching the video in a quiet indoor or noisy outdoor environment, enhancing the immersive experience of the video content.

[0064] As an optional but non-limiting implementation, the volume loudness equalization parameters of the target loudness model are adjusted based on different reference volume attribute information, including the following steps B1-B2:

[0065] Step B1: Based on different reference volume attribute information, determine the volume loudness equalization effect of the target loudness model on the target video as the playback volume of the target video increases.

[0066] Step B2: Based on the volume loudness equalization effect of the target loudness model on the target video, adjust the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video.

[0067] As videos are played more frequently on the platform, the number of views for the target video will continuously increase. Changes in the number of views for the target video are usually accompanied by an expanding and more diverse audience, as well as increased complexity in the viewing environment. These changes may affect the required volume loudness when playing the target video. The volume loudness equalization effect of the target loudness model on the target video refers to the actual volume loudness balance achieved after the target loudness model performs volume loudness equalization processing on the target video. For example, can it ensure that dialogue in the video is clearly audible, while the loudness of background music and sound effects is neither too loud and interferes with the dialogue, nor too soft and loses its intended atmosphere? This effect can be evaluated in various ways, such as the feedback on the volume loudness of the target video after volume loudness equalization, and statistical data on the volume adjustment operations of the target video after volume loudness equalization.

[0068] See Figure 3 and Figure 4 This analysis process evaluates the volume equalization effect of the target loudness model as the playback volume of the target video increases, based on different reference volume attribute information. It involves observing whether the volume is adjusted at different playback stages to understand the performance of the loudness model in controlling the target video's volume under different propagation levels. For example, if the volume of the target video is frequently increased after volume equalization during playback, it may indicate a problem with the model's equalization effect at this stage, resulting in an overall soft sound. If the volume equalization effect of the target loudness model on the target video is found to be poor, such as the sound being too soft or too heavy, or an imbalance in the loudness ratio of different audio elements, parameters need to be modified to improve this situation. For example, if the overall sound is found to be too soft, it may be necessary to increase the gain parameters of certain frequency bands or adjust the overall loudness reference value so that the loudness model can output a more suitable volume in subsequent volume equalization processing.

[0069] As the number of views of a target video increases, the composition of the audience and the viewing environment often change. In this way, the target loudness model can dynamically adapt to these changes. For example, when a video first begins to circulate, it may primarily be watched by a specific interest group; as views increase, more general viewers begin to watch. Different groups may have different preferences for volume loudness. This method can adjust model parameters based on reference volume attribute information at different stages, ensuring that the volume loudness equalization effect consistently meets the needs of different viewers. Continuous monitoring and parameter adjustment of the volume loudness equalization effect ensures that the target video maintains good popularity throughout its playback. By continuously adjusting parameters based on actual playback conditions, the target loudness model can more accurately perform volume loudness equalization on the target video. This adaptive adjustment mechanism allows the target loudness model to maintain good performance throughout the entire lifecycle of the target video. In other words, even if the number of views changes significantly, the loudness model can still adapt by adjusting parameters, thereby reducing the possibility of needing to redevelop or replace the loudness model due to model failure, reducing costs and improving efficiency.

[0070] The technical solution of this disclosure addresses the issue that during the playback of a target video, the diversity of the audience and the complexity of the viewing environment change as the number of plays increases. By dividing the playback into stages based on different play counts and determining the reference volume attribute information for each stage, the differences in audio performance under different propagation levels can be accurately captured. Each reference volume attribute information details the volume loudness adjustment operation of the target video when starting playback from different baseline volume levels, providing in-depth understanding of the volume adjustment of the target video under different initial volume settings. Moreover, the reference volume attribute information is based on actual playback data collection, accurately reflecting the volume loudness adjustment requirements of the target video at each playback stage. Thus, during the volume loudness equalization process, the target loudness model may degrade due to various factors, such as changes in audio content, audience, and playback environment. By continuously adjusting the volume loudness equalization parameters of the target loudness model based on the reference volume attribute information at different playback stages, problems that may lead to the failure of the volume loudness equalization strategy can be promptly identified and resolved. This avoids the problem of the volume loudness equalization strategy failing due to its inability to adapt to changes throughout the entire playback cycle, ensuring that the video provides a good volume loudness effect at each playback stage.

[0071] Figure 5 This is a flowchart illustrating another loudness model failure prevention method provided in this embodiment. The technical solution of this embodiment further optimizes the process of determining the volume loudness equalization effect of the target loudness model on the target video as the playback volume of the target video increases based on different reference volume attribute information in the aforementioned embodiments. This embodiment can be combined with various optional solutions in one or more of the above embodiments.

[0072] like Figure 5 As shown, the loudness model failure prevention method of this disclosure embodiment may include the following process:

[0073] S510. Determine the different reference volume attribute information of the target video when it is played in different playback stages. The target video is a video that has undergone volume loudness equalization processing using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volumes reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different baseline volume loudness in each playback stage.

[0074] S520. For the reference volume attribute information corresponding to different playback stages, based on the number of times the target video's volume was turned down and the number of times its volume was turned up recorded in the reference volume attribute information, determine the reference volume adjustment probability change information corresponding to the target video. The reference volume adjustment probability change information is used to indicate the probability change trend of the target video being adjusted again during playback as the playback volume of the target video increases.

[0075] See Figure 2 and Figure 3 The number of times the target video's volume was turned down and the number of times its volume was turned up are key elements in the reference volume attribute information. The number of times the video's volume was turned down indicates the number of times the video's sound was perceived as too loud and therefore the volume was turned down during playback and at the baseline volume. Conversely, the number of times the video's volume was turned up reflects the number of times the video's sound was perceived as too quiet and therefore the volume was turned up. By observing these numbers, one can intuitively understand the target loudness model's satisfaction with the video's volume equalization effect and the direction of volume adjustment needs.

[0076] See Figure 2 and Figure 3 The reference volume adjustment probability change information for the target video can be derived from the number of times the target video's volume was turned down and the number of times its volume was turned up. This information describes the changing trend of the likelihood of adjusting the target video's volume again during playback, after volume equalization processing, as the number of plays increases. For example, if the number of times the volume is turned up gradually increases with the number of plays, it may mean that the probability of turning up the target video's volume in subsequent playbacks is increasing; conversely, if the number of times the volume is turned up gradually decreases with the number of plays, it may mean that the probability of turning up the target video's volume in subsequent playbacks is decreasing. This probability change trend is part of the reference volume adjustment probability change information.

[0077] See Figure 2 and Figure 3 For each playback stage, the system analyzes the reference volume attribute information, noting the number of times the target video's volume was reduced and increased. By comprehensively analyzing this data across different playback stages, the system determines the changes in the probability of reference volume adjustment. For example, by comparing the number of volume increases and decreases between lower and higher playback stages, the system observes the changing patterns to determine how the probability of adjusting the volume of a target video that has already undergone volume equalization changes as playback volume increases—whether it increases, decreases, or remains stable.

[0078] Determining the probability change information of reference volume adjustment provides a strong basis for optimizing the loudness equalization strategy. Understanding the trend of the probability of the target video's volume being adjusted again allows for targeted adjustments to the loudness equalization parameters. If it is found that the probability of the target video's volume being adjusted increases with the number of plays, it may mean that the current loudness equalization strategy is insufficient to cope with more diverse playback needs and environments, requiring improvement. This could involve adjusting the loudness ratio of different audio elements or optimizing the overall volume baseline to better meet video playback requirements. During the dissemination of the target video, the audience and playback environment will continuously change; the reference volume adjustment probability change information can help the system adapt to these changes.

[0079] As an optional but non-limiting implementation, the reference volume adjustment probability change information corresponding to the target video is determined based on the number of times the target video's volume was decreased and increased, as recorded in the reference volume attribute information. This includes the following steps C1-C2:

[0080] Step C1: Based on the reference volume attribute information corresponding to each playback stage, which records the number of times the target video's volume was reduced and increased when it started playing at different reference volume levels, determine the probability distribution of volume adjustment for the target video when it starts playing at different reference volume levels in each playback stage.

[0081] Step C2: Based on the probability distribution of volume adjustment when the target video starts playing at different baseline volume levels in each playback stage, determine the mean probability of volume adjustment of the target video in each playback stage.

[0082] Step C3: Based on the average probability of volume adjustment in each playback stage of the target video, determine the reference volume adjustment probability change information corresponding to the target video.

[0083] See Figure 2 and Figure 3For each playback stage, the reference volume attribute information records the number of times the target video's volume was reduced and increased when it started playing at different reference volume levels. Based on this data, the probability of the target video being adjusted (increased or decreased) when it started playing at a certain reference volume level during a specific playback stage can be calculated. For example, in one playback stage, if the target video is played at 50% of the reference volume level, the number of times the target video was reduced after volume equalization is recorded as 30 times, and the number of times it was increased after volume equalization is recorded as 20 times, with a total of 100 playbacks. Therefore, the probability of reducing the volume is 30 / 100 = 0.3, and the probability of increasing the volume is 20 / 100 = 0.2. Repeating this calculation process for different reference volume levels yields the probability distribution of the target video being adjusted when it started playing at different reference volume levels during a playback stage.

[0084] See Figure 2 and Figure 3 After determining the probability distribution of volume adjustment when starting playback at different baseline volume levels in each playback stage, the mean probability for that playback stage is calculated. The mean probability is a comprehensive measure of the various possible volume adjustment probabilities for that playback stage. For example, if a playback stage has multiple different baseline volume levels corresponding to volume adjustment probabilities, summing these probability values ​​and dividing by the number of baseline volume levels yields the mean probability of volume adjustment for the target video in that playback stage. This mean reflects the average likelihood of volume adjustment for the target video as a whole during that playback stage.

[0085] See Figure 2 and Figure 3Based on the average volume adjustment probability calculated at each playback stage of the target video, the reference volume adjustment probability change information for the target video is determined. By comparing the average probability at different playback stages, it can be seen how the probability of the target video being volume adjusted changes as the number of plays increases. For example, if the average probability gradually increases from one playback stage to the next, it indicates that the likelihood of the target video being volume adjusted increases with the number of plays; if the average probability remains stable, it indicates that the operation of volume adjustment of the target video has not changed significantly; if the average probability decreases, it indicates that the likelihood of the target video being volume adjusted decreases. For example, closely monitor the probability distribution of S_1w and S_2w, and ensure that the average volume adjustment probability avg(S_2w) corresponding to S_2w is significantly less than the average volume adjustment probability avg(S_1w) corresponding to S_1w. If the average volume adjustment probability avg(S_1w) corresponding to S_1w is significantly less than the average volume adjustment probability avg(S_2w) corresponding to S_2w, an alarm mechanism should be triggered. This indicates that the loudness model's effect on volume loudness equalization of the target video has not met the expected standard during application. Specifically, bad cases where S_1w is much smaller than S_2w can be collected periodically. Based on these cases, effect analysis can be conducted to improve the loudness model's performance in volume loudness equalization, ensuring the online model has good anti-degradation and adaptive capabilities.

[0086] By employing the above method and determining the probability distribution and mean, we can accurately understand the likelihood of a target video being adjusted in volume at different playback stages and under different baseline volume levels. This provides accurate data support for optimizing volume loudness equalization. Based on the calculated probability mean and probability change information, the volume loudness equalization parameters of the target loudness model can be adjusted accordingly. If a higher probability of volume adjustment is found in a certain playback stage, it may mean that the current loudness setting is not appropriate and parameters need to be adjusted to improve the video playback effect. For example, if the probability mean shows a higher probability of volume increase in a certain playback stage, the baseline volume loudness of that stage can be appropriately increased or the audio gain adjusted to make the video playback more in line with the expected auditory effect. Furthermore, accurate probability analysis can help reduce the frequency of video adjustments due to inappropriate volume during playback, thereby improving video playback stability.

[0087] S530. Based on the reference volume adjustment probability change information corresponding to the target video, determine whether the volume loudness equalization of the target loudness model for the target video deteriorates as the number of plays of the target video increases. The deterioration of the volume loudness equalization indicates that the effect of maintaining a relative balance of the volume loudness of each part of the target video during the volume loudness equalization process gradually deteriorates as the number of plays of the target video increases.

[0088] See Figure 2 The reference volume adjustment probability change information is obtained by analyzing the volume adjustment behavior of the target video at different playback stages. It reflects the trend of the probability of adjusting the volume of the target video as the number of views increases. For example, if the target video is rarely adjusted in the early stages of playback, but the probability of the volume being turned up or down increases significantly as the number of views increases, this indicates that there may be factors at some playback stages that cause the video volume to be unsuitable.

[0089] See Figure 2 The purpose of the target loudness model is to equalize the volume of a target video, ensuring that all parts of the video (such as dialogue, background music, and sound effects) have a relatively balanced volume during playback. This avoids situations where some sounds are too loud or too soft, thus providing a good playback experience from an auditory perspective. Volume loudness equalization degradation refers to the situation where, during the volume loudness equalization process, the initial effect of maintaining a relatively balanced volume across different parts of the target video gradually deteriorates as the video is played more. Specifically, this manifests as an increased frequency of volume adjustments or more pronounced volume differences between different parts of the video. For example, initially, a comfortable listening experience may not require frequent volume adjustments, but as playback increases, sudden increases or decreases in volume may occur in certain scenes, necessitating frequent volume adjustments. This indicates potential volume loudness equalization degradation.

[0090] See Figure 2 The system uses changes in the reference volume adjustment probability to determine whether the target loudness model's volume balance for the target video has deteriorated. If the reference volume adjustment probability change information shows a significant increase in the probability of volume adjustment as playback volume increases, it likely indicates that the target loudness model's volume balance performance is worsening, i.e., volume balance degradation has occurred. For example, if the probability of increasing the volume increases from 10% to 30% and the probability of decreasing the volume increases from 5% to 20% from one playback stage to the next, this suggests that the target loudness model may not be maintaining the video's volume balance well during this stage, indicating degradation.

[0091] This approach allows for the timely detection of potential issues with the target loudness model during volume loudness equalization. During video playback, changes in the audience and viewing environment, along with increased playback volume, can cause an initially effective loudness model to gradually become ineffective. By analyzing changes in the probability of reference volume adjustments, degradation can be detected early, allowing for appropriate adjustments and optimizations. Once volume loudness equalization degradation is identified, the target loudness model can be specifically optimized. Adjusting the model's parameters, algorithms, or strategies based on the specific manifestations of degradation improves the effectiveness of volume loudness equalization. Timely detection and resolution of volume loudness equalization degradation significantly enhances video playback quality, eliminating the need for frequent manual volume adjustments.

[0092] As an optional but non-limiting implementation, based on the reference volume adjustment probability change information corresponding to the target video, it is determined whether the volume loudness equalization of the target loudness model for the target video deteriorates as the playback volume of the target video increases. This includes the following steps D1-D2:

[0093] Step D1: If, based on the reference volume adjustment probability change information corresponding to the target video, it is determined that the probability of adjusting the volume of the target video at each playback stage is increasing, then it is determined that as the playback volume of the target video continues to increase, the volume loudness balance of the target loudness model for the target video deteriorates.

[0094] Step D2: If, based on the reference volume adjustment probability change information corresponding to the target video, it is determined that the probability of adjusting the volume of the target video in each playback stage has not increased, then it is determined that the volume loudness balance of the target loudness model for the target video has not deteriorated as the playback volume of the target video continues to increase.

[0095] See Figure 3 The reference volume adjustment probability change information describes the change in the probability of adjusting the volume of the target video at different playback stages. If, based on this reference volume adjustment probability change information, the probability of adjusting the volume of the target video at each playback stage is increasing, it means that as the number of playbacks increases, the volume of the target video will be adjusted more and more frequently. This indicates that the target loudness model has a problem with the volume loudness equalization of the target video, that is, a deterioration in volume loudness equalization has occurred.

[0096] See Figure 3Similarly, based on the reference volume adjustment probability change information, if it is determined that the probability of volume adjustment of the target video does not increase in each playback stage, it indicates that the video volume adjustment does not change significantly or changes very little as the playback volume increases. In this case, it can be considered that the target loudness model has not deteriorated in its volume loudness balance for the target video. This means that the target loudness model can maintain the volume loudness balance of the video well in different playback stages, and there is no need to frequently adjust it manually to improve the auditory experience.

[0097] By analyzing the changes in the probability of reference volume adjustment, it's possible to accurately determine whether the model's performance declines with increasing video playback. This accurate assessment helps in taking timely measures to address the problem and improve the video's audio quality. Based on the assessment results, the target loudness model can be optimized in a targeted manner. If degradation is confirmed, the causes can be analyzed in depth, such as changes in video content, audience demographics, or playback environment. Then, the model's parameters, algorithms, or strategies can be adjusted to address the specific problem. By promptly identifying and resolving volume loudness equalization degradation issues, frequent adjustments due to inappropriate volume during video viewing can be avoided, extending the effectiveness of the volume loudness equalization strategy.

[0098] S540. Based on the volume loudness equalization effect of the target loudness model on the target video, adjust the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video, so as to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0099] As an optional but non-limiting implementation, based on the volume loudness equalization effect of the target loudness model on the target video, the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video are adjusted, including the following steps E1-E2:

[0100] Step E1: If the volume loudness equalization of the target loudness model for the target video does not deteriorate as the number of views of the target video increases, then the volume loudness equalization parameters used by the target loudness model will not be adjusted.

[0101] Step E2: If the volume loudness equalization of the target loudness model deteriorates as the number of views of the target video increases, the volume loudness equalization parameters used by the target loudness model are adjusted to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0102] See Figure 3When it is determined that the target loudness model does not deteriorate in its volume loudness equalization of the target video as the number of plays increases, it means that the target loudness model is currently maintaining the video's volume loudness balance well at different playback stages, and there is no need to adjust its volume loudness equalization parameters. For example, if analysis of the reference volume adjustment probability changes shows that the probability of volume adjustment does not increase at each playback stage, it indicates that the volume loudness of the target video is satisfactory. In this case, keeping the volume loudness equalization parameters of the target loudness model unchanged can avoid the potential risks and resource waste caused by unnecessary adjustments.

[0103] See Figure 3 If it's determined that the target loudness model's volume equalization for the target video may be deteriorating as the playback volume increases, then the volume equalization parameters used by the target loudness model need to be adjusted. Adjusting the volume equalization parameters of the target loudness model can suppress performance degradation in volume equalization. For example, if the probability of volume adjustment increases at various playback stages, it indicates that the video's volume loudness balance may be disrupted. In this case, adjusting the volume equalization parameters of the target loudness model, such as adjusting audio gain and compression ratio, can improve the volume equalization effect and restore its suitability.

[0104] By adopting the above method, parameter adjustments are not made when degradation occurs, avoiding unnecessary computation and resource consumption and improving system efficiency. Maintaining parameter stability also helps preserve the consistency and stability of video and audio, preventing audio quality fluctuations caused by frequent parameter adjustments and ensuring smooth playback of the target video in a stable audio environment. Furthermore, parameter adjustments when degradation occurs can promptly address volume and loudness equalization issues, improving the performance of the target loudness model. Targeted parameter adjustments allow for rapid adaptation to changes during video playback, meeting evolving playback demands. This process endows the target loudness model with adaptive adjustment capabilities, automatically determining whether parameter adjustments are necessary based on video playback feedback, thus better adapting to different playback scenarios and requirements, and addressing degradation issues or maintaining good audio quality through timely parameter adjustments.

[0105] The technical solution of this disclosure addresses the issue that during the playback of a target video, the diversity of the audience and the complexity of the viewing environment change as the number of plays increases. By dividing the playback into stages based on different play counts and determining the reference volume attribute information for each stage, the differences in audio performance under different propagation levels can be accurately captured. Each reference volume attribute information details the volume loudness adjustment operation of the target video when starting playback from different baseline volume levels, providing a deeper understanding of the volume adjustment of the target video under different initial volume settings. The volume attribute accurately reflects the volume loudness adjustment requirements of the target video at each playback stage. Thus, during the volume loudness equalization process, the target loudness model may degrade due to various factors, such as changes in audio content, audience, and playback environment. By continuously adjusting the volume loudness equalization parameters of the target loudness model based on the reference volume attribute information at different playback stages, problems that may cause the volume loudness equalization strategy to fail can be identified and resolved in a timely manner. This avoids the problem of the volume loudness equalization strategy failing due to its inability to adapt to changes throughout the entire playback cycle, ensuring that the video provides a good volume loudness effect at each playback stage.

[0106] Figure 6 This is a schematic diagram of a loudness model anti-failure device provided in an embodiment of the present disclosure. The present disclosure is applicable to monitoring the volume loudness equalization effect of the volume loudness equalization strategy used in video playback to prevent the volume loudness effect from gradually deteriorating due to the failure of the volume loudness equalization strategy. The loudness model anti-failure device can be implemented in the form of software and / or hardware, and is generally integrated on any electronic device with network communication function, such as a mobile terminal, PC, or server.

[0107] like Figure 6 As shown, the loudness model anti-failure device in this embodiment may include the following:

[0108] The determination module 610 is used to determine different reference volume attribute information of the target video when it is played in different playback stages. The target video is a video that is processed by volume loudness equalization using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volume reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different reference volume loudness in each playback stage.

[0109] The adjustment module 620 is used to adjust the volume loudness equalization parameters of the target loudness model according to different reference volume attribute information, so as to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0110] Based on the above embodiments, optionally, determining different reference volume attribute information when the target video is played at different playback stages includes:

[0111] The first reference volume attribute information of the target video in the first playback stage is determined. The first playback stage is the playback stage corresponding to the target video from the first playback until the number of playbacks of the target video reaches the first number of playbacks. The first reference volume attribute information is used to describe the number of times the target video is reduced in volume and the number of times the volume is increased when the target video starts playing at different reference volume levels in the first playback stage.

[0112] A second reference volume attribute information for the target video is determined in the second playback stage. The second playback stage is the playback stage corresponding to when the playback volume of the target video reaches the first playback volume and when the playback volume of the target video reaches the second playback volume. The second reference volume attribute information is used to describe the number of times the volume of the target video is reduced and the number of times the volume is increased when the target video starts playing at different reference volume levels in the second playback stage. The first playback volume is less than the second playback volume.

[0113] Based on the above embodiments, optionally, the volume loudness equalization parameters of the target loudness model are adjusted according to different reference volume attribute information, including:

[0114] Based on different reference volume attribute information, the volume loudness equalization effect of the target loudness model on the target video is determined as the playback volume of the target video increases.

[0115] Based on the volume loudness equalization effect of the target loudness model on the target video, the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video are adjusted.

[0116] Based on the above embodiments, optionally, according to different reference volume attribute information, the volume loudness equalization effect of the target loudness model on the target video as the playback volume of the target video increases is determined, including:

[0117] For reference volume attribute information corresponding to different playback stages, the reference volume adjustment probability change information corresponding to the target video is determined based on the number of times the target video's volume was turned down and the number of times its volume was turned up recorded in the reference volume attribute information. The reference volume adjustment probability change information is used to indicate the probability change trend of the target video being adjusted again during playback after volume equalization as the playback volume of the target video continues to increase.

[0118] Based on the reference volume adjustment probability change information corresponding to the target video, it is determined whether the volume loudness equalization of the target loudness model for the target video deteriorates as the playback volume of the target video increases. The deterioration of the volume loudness equalization indicates that the effect of maintaining a relative balance of the volume loudness of each part of the target video during the volume loudness equalization process gradually worsens as the playback volume of the target video increases.

[0119] Based on the above embodiments, optionally, the reference volume adjustment probability change information corresponding to the target video is determined according to the number of times the target video's volume was reduced and the number of times its volume was increased recorded in the reference volume attribute information, including:

[0120] Based on the reference volume attribute information corresponding to each playback stage, which records the number of times the target video's volume was reduced and increased when it started playing at different baseline volume levels, the probability distribution of volume adjustment for the target video when it started playing at different baseline volume levels in each playback stage is determined.

[0121] Based on the probability distribution of volume adjustment of the target video when it starts playing at different baseline volume loudness in each playback stage, the mean probability of volume adjustment of the target video in each playback stage is determined.

[0122] Based on the average probability of volume adjustment in the target video at each playback stage, the reference volume adjustment probability change information corresponding to the target video is determined.

[0123] Based on the above embodiments, optionally, according to the reference volume adjustment probability change information corresponding to the target video, determining whether the volume loudness equalization of the target loudness model for the target video deteriorates as the playback volume of the target video increases includes:

[0124] If, based on the reference volume adjustment probability change information corresponding to the target video, it is determined that the probability of adjusting the volume of the target video at each playback stage is increasing, then it is determined that as the playback volume of the target video continues to increase, the target loudness model exhibits a deterioration in the volume loudness balance of the target video.

[0125] If, based on the reference volume adjustment probability change information corresponding to the target video, it is determined that the probability of adjusting the volume of the target video at each playback stage has not increased, then it is determined that as the playback volume of the target video continues to increase, the target loudness model does not exhibit volume loudness balance degradation in the target video.

[0126] Based on the above embodiments, optionally, according to the volume loudness equalization effect of the target loudness model on the target video, the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video are adjusted, including:

[0127] If the target loudness model does not show any degradation in volume loudness equalization as the number of plays of the target video increases, then the volume loudness equalization parameters used by the target loudness model will not be adjusted.

[0128] If the target loudness model experiences a deterioration in volume loudness equalization as the number of plays of the target video increases, the volume loudness equalization parameters used by the target loudness model will be adjusted to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0129] The technical solution of this disclosure addresses the issue that during the playback of a target video, the diversity of the audience and the complexity of the viewing environment change as the number of plays increases. By dividing the playback into stages based on different play counts and determining the reference volume attribute information for each stage, the differences in audio performance under different propagation levels can be accurately captured. Each reference volume attribute information details the volume loudness adjustment operation of the target video when starting playback from different baseline volume levels, providing in-depth understanding of the volume adjustment of the target video under different initial volume settings. Moreover, the reference volume attribute information is based on actual playback data collection, accurately reflecting the volume loudness adjustment requirements of the target video at each playback stage. Thus, during the volume loudness equalization process, the target loudness model may degrade due to various factors, such as changes in audio content, audience, and playback environment. By continuously adjusting the volume loudness equalization parameters of the target loudness model based on the reference volume attribute information at different playback stages, problems that may lead to the failure of the volume loudness equalization strategy can be promptly identified and resolved. This avoids the problem of the volume loudness equalization strategy failing due to its inability to adapt to changes throughout the entire playback cycle, ensuring that the video provides a good volume loudness effect at each playback stage.

[0130] The loudness model failure prevention device provided in this disclosure can execute the loudness model failure prevention method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects for executing the loudness model failure prevention method.

[0131] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0132] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Reference is made below. Figure 7 It illustrates an electronic device suitable for implementing embodiments of the present disclosure (e.g., Figure 7 The diagram below shows the structure of the terminal device or server 500. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0133] like Figure 7 As shown, electronic device 500 may include a processing unit (e.g., central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An edit / output (I / O) interface 505 is also connected to bus 504.

[0134] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0135] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0136] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0137] The electronic device provided in this embodiment and the loudness model anti-failure method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0138] This disclosure provides a computer storage medium storing a computer program that, when executed by a processor, implements the loudness model anti-failure method provided in the above embodiments.

[0139] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0140] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0141] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0142] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: determine different reference volume attribute information for a target video played at different playback stages, wherein the target video is a video processed by volume loudness equalization using a target loudness model, each playback stage is a different playback volume interval divided according to the different playback volumes reached sequentially during playback of the target video, and each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different reference volume loudness in each playback stage; and adjust the volume loudness equalization parameters of the target loudness model according to the different reference volume attribute information to suppress the performance degradation of the target loudness model in volume loudness equalization.

[0143] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0145] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0146] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0147] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0148] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0149] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0150] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for preventing failure of a loudness model, characterized in that, The method includes: The reference volume attribute information of the target video is determined when the target video is played in different playback stages. The target video is a video that is processed by volume loudness equalization using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volume reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different reference volume loudness in each playback stage. Based on different reference volume attribute information, the volume loudness equalization parameters of the target loudness model are adjusted to suppress the performance degradation of the target loudness model in volume loudness equalization.

2. The method according to claim 1, characterized in that, Determine the different reference volume attribute information of the target video when it is played at different stages of playback, including: The first reference volume attribute information of the target video in the first playback stage is determined. The first playback stage is the playback stage corresponding to the target video from the first playback until the number of playbacks of the target video reaches the first number of playbacks. The first reference volume attribute information is used to describe the number of times the target video is reduced in volume and the number of times the volume is increased when the target video starts playing at different reference volume levels in the first playback stage. A second reference volume attribute information for the target video is determined in the second playback stage. The second playback stage is the playback stage corresponding to when the playback volume of the target video reaches the first playback volume and when the playback volume of the target video reaches the second playback volume. The second reference volume attribute information is used to describe the number of times the volume of the target video is reduced and the number of times the volume is increased when the target video starts playing at different reference volume levels in the second playback stage. The first playback volume is less than the second playback volume.

3. The method according to claim 1, characterized in that, Based on different reference volume attribute information, the volume loudness equalization parameters of the target loudness model are adjusted, including: Based on different reference volume attribute information, the volume loudness equalization effect of the target loudness model on the target video is determined as the playback volume of the target video increases. Based on the volume loudness equalization effect of the target loudness model on the target video, the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video are adjusted.

4. The method according to claim 3, characterized in that, Based on different reference volume attribute information, the volume loudness equalization effect of the target loudness model on the target video is determined as the playback volume of the target video increases, including: For reference volume attribute information corresponding to different playback stages, the reference volume adjustment probability change information corresponding to the target video is determined based on the number of times the target video's volume was turned down and the number of times its volume was turned up recorded in the reference volume attribute information. The reference volume adjustment probability change information is used to indicate the probability change trend of the target video being adjusted again during playback after volume equalization as the playback volume of the target video continues to increase. Based on the reference volume adjustment probability change information corresponding to the target video, it is determined whether the volume loudness equalization of the target loudness model for the target video deteriorates as the playback volume of the target video increases. The deterioration of the volume loudness equalization indicates that the effect of maintaining a relative balance of the volume loudness of each part of the target video during the volume loudness equalization process gradually worsens as the playback volume of the target video increases.

5. The method according to claim 4, characterized in that, Based on the number of times the target video's volume was decreased and increased as recorded in the reference volume attribute information, the reference volume adjustment probability change information corresponding to the target video is determined, including: Based on the reference volume attribute information corresponding to each playback stage, which records the number of times the target video's volume was reduced and increased when it started playing at different baseline volume levels, the probability distribution of volume adjustment for the target video when it started playing at different baseline volume levels in each playback stage is determined. Based on the probability distribution of volume adjustment of the target video when it starts playing at different baseline volume loudness in each playback stage, the mean probability of volume adjustment of the target video in each playback stage is determined. Based on the average probability of volume adjustment in the target video at each playback stage, the reference volume adjustment probability change information corresponding to the target video is determined.

6. The method according to claim 5, characterized in that, Based on the reference volume adjustment probability change information corresponding to the target video, determine whether the target loudness model experiences volume loudness equalization degradation as the playback volume of the target video increases, including: If, based on the reference volume adjustment probability change information corresponding to the target video, it is determined that the probability of adjusting the volume of the target video at each playback stage is increasing, then it is determined that as the playback volume of the target video continues to increase, the target loudness model exhibits a deterioration in the volume loudness balance of the target video. If, based on the reference volume adjustment probability change information corresponding to the target video, it is determined that the probability of adjusting the volume of the target video at each playback stage has not increased, then it is determined that as the playback volume of the target video continues to increase, the target loudness model does not exhibit volume loudness balance degradation in the target video.

7. The method according to claim 4, characterized in that, Based on the volume loudness equalization effect of the target loudness model on the target video, the volume loudness equalization parameters used by the target loudness model when performing volume loudness equalization on the target video are adjusted, including: If the target loudness model does not show any degradation in volume loudness equalization as the number of plays of the target video increases, then the volume loudness equalization parameters used by the target loudness model will not be adjusted. If the target loudness model experiences a deterioration in volume loudness equalization as the number of plays of the target video increases, the volume loudness equalization parameters used by the target loudness model will be adjusted to suppress the performance degradation of the target loudness model in volume loudness equalization.

8. A loudness model anti-failure device, characterized in that, The device includes: The determination module is used to determine different reference volume attribute information of the target video when it is played in different playback stages. The target video is a video that has undergone volume loudness equalization processing using a target loudness model. Each playback stage is a different playback volume interval divided according to the different playback volumes reached in sequence when the target video is played. Each reference volume attribute information is used to describe the volume loudness adjustment operation performed on the target video when it starts playing at a different reference volume loudness in each playback stage. The adjustment module is used to adjust the volume loudness equalization parameters of the target loudness model according to different reference volume attribute information, so as to suppress the performance degradation of the target loudness model in volume loudness equalization.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the loudness model anti-failure method as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the loudness model anti-failure method as described in any one of claims 1-7.

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