Audio recognition method and device, medium and computing equipment

Abstract

The embodiment of the invention provides an audio recognition method, and relates to the technical field of data processing. The audio recognition method comprises the following steps: acquiring a second audio obtained by internally recording a first audio by terminal equipment and a third audio obtained by externally recording the first audio by the terminal equipment; determining scene information of recording the first audio by the terminal equipment; determining a first audio feature of the second audio and a second audio feature of the third audio; and according to the scene information, the first audio feature and the second audio feature, determining a target audio matched with the first audio, and determining information of the target audio as identification information of the first audio. Through audio feature processing of internally recorded and externally recorded audio and comprehensive use of scene information, audio fingerprints with higher quality and consistency are generated, the recognition rate of a song listening and identifying function is greatly improved, and better experience is brought to a user. In addition, the embodiment of the invention provides an audio recognition device, a medium and computing equipment.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of data processing technology, and more specifically, the embodiments of the present disclosure relate to an audio recognition method, apparatus, medium, and computing device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the present disclosure. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] Song recognition by listening means that when a user hears an unknown sound source, the corresponding song information (including song title, singer, lyrics, etc.) is identified by collecting the sound source and returning it to the user. In related technologies, recognition is often performed by collecting audio data and extracting audio fingerprints.

[0004] However, the above implementation method is too simple and ignores many unstable factors encountered when collecting audio data, resulting in inaccurate recognition. Summary of the invention

[0005] The present disclosure provides an audio recognition method, apparatus, medium and computing device to solve the technical problem of inaccurate song recognition in the related art.

[0006] In a first aspect of the embodiments of the present disclosure, there is provided an audio recognition method, comprising:

[0007] Acquire a second audio obtained by internally recording the first audio by a terminal device and a third audio obtained by externally recording the first audio by the terminal device;

[0008] Determine scenario information in which the terminal device records the first audio, where the scenario information includes at least one of a device type of the terminal device, noise in an environment in which the terminal device is located, and an audio output mode in which the terminal device plays the first audio;

[0009] determining a first audio characteristic of the second audio and a second audio characteristic of the third audio;

[0010] According to the scene information, the first audio feature, and the second audio feature, a target audio that matches the first audio is determined, and information of the target audio is determined as identification information of the first audio.

[0011] In one embodiment of the present disclosure, determining the target audio that matches the first audio according to the scene information, the first audio feature, and the second audio feature includes:

[0012] Determine a first similarity between the first audio feature and a stored internally recorded audio feature of a fourth audio, and determine a second similarity between the second audio feature and an externally recorded audio fingerprint of the fourth audio;

[0013] configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information;

[0014] Determine a confidence level between the first audio and the fourth audio according to the first similarity, the first weight, the second similarity, and the second weight;

[0015] In response to the confidence level being greater than a preset threshold, the fourth audio is determined as the target audio.

[0016] In another embodiment of the present disclosure, the scene information includes the noise and the audio output mode, and configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information, includes:

[0017] Determining a noise interval in which the noise is located;

[0018] According to the noise interval and the audio output mode, a first weight is configured for the first similarity and a second weight is configured for the second similarity.

[0019] In yet another embodiment of the present disclosure, configuring a first weight for the first similarity and configuring a second weight for the second similarity according to the noise interval and the audio output mode includes:

[0020] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is an external speaker mode, setting the first weight to be smaller than the second weight;

[0021] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight;

[0022] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0023] In yet another embodiment of the present disclosure, configuring a first weight for the first similarity and configuring a second weight for the second similarity according to the noise interval and the audio output mode includes:

[0024] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is an external speaker mode, setting the first weight to be equal to the second weight;

[0025] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight;

[0026] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0027] In another embodiment of the present disclosure, configuring a first weight for the first similarity and configuring a second weight for the second similarity according to the noise interval and the audio output mode includes:

[0028] In response to the noise interval indicating that the environment in which the terminal device is located is a high-noise environment, the first weight is set to be greater than the second weight.

[0029] In yet another embodiment of the present disclosure, determining a first similarity between the first audio feature and a stored internally recorded audio feature of a fourth audio comprises:

[0030] Acquire a target parameter, where the target parameter includes at least one of a current time period and a location of the terminal device;

[0031] An audio database matching the target parameter is determined, and a fourth audio stored in the audio database is determined.

[0032] In yet another embodiment of the present disclosure, the determining the first audio feature of the second audio and the second audio feature of the third audio includes:

[0033] Obtaining a first proportion of human voice and a second proportion of non-human voice in the first audio;

[0034] Determining an audio feature recognition strategy according to the first proportion and the second proportion;

[0035] The audio feature recognition strategy is adopted to recognize the first audio feature of the second audio and the second audio feature of the third audio.

[0036] In another embodiment of the present disclosure, determining the audio feature recognition strategy according to the first proportion and the second proportion includes:

[0037] In response to the first proportion being less than a first preset proportion, or the first proportion being less than a second preset proportion and the second proportion being greater than a third preset proportion, determining the recognition mode of song recognition by listening to songs as the audio feature recognition strategy;

[0038] In response to the second proportion being less than the third preset proportion, or the second proportion being not less than the third preset proportion and the first proportion being not less than the second preset proportion, the recognition mode of identifying songs by humming is determined as the audio feature recognition strategy.

[0039] In yet another embodiment of the present disclosure, the adopting the audio feature recognition strategy to recognize the first audio feature of the second audio and the second audio feature of the third audio includes:

[0040] In response to the audio feature recognition strategy being a recognition mode of humming to identify a song, the recognition mode of humming to identify a song is used to identify the second audio and the third audio for a preset time length, and then the recognition mode of lyrics to identify a song is used to identify the second audio and the third audio to obtain the first audio feature and the second audio feature.

[0041] In yet another embodiment of the present disclosure, the start playing time point of the second audio is the same as that of the third audio.

[0042] In a second aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method described in the first aspect and any one of the embodiments is implemented.

[0043] In a third aspect of the embodiments of the present disclosure, there is provided an audio recognition device, comprising:

[0044] An acquisition module, used to acquire a second audio obtained by internally recording the first audio by a terminal device and a third audio obtained by externally recording the first audio by the terminal device;

[0045] A first determining module is used to determine scenario information of the terminal device recording the first audio, where the scenario information includes at least one of a device type of the terminal device, noise in an environment where the terminal device is located, and an audio output mode of the terminal device playing the first audio;

[0046] A second determining module, configured to determine a first audio feature of the second audio and a second audio feature of the third audio;

[0047] The third determination module is used to determine the target audio that matches the first audio according to the scene information, the first audio feature and the second audio feature, and determine the information of the target audio as the identification information of the first audio.

[0048] In one embodiment of the present disclosure, the third determination module determines the target audio that matches the first audio according to the scene information, the first audio feature, and the second audio feature, and is specifically used to:

[0049] Determine a first similarity between the first audio feature and a stored internally recorded audio feature of a fourth audio, and determine a second similarity between the second audio feature and an externally recorded audio fingerprint of the fourth audio;

[0050] configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information;

[0051] Determine a confidence level between the first audio and the fourth audio according to the first similarity, the first weight, the second similarity, and the second weight;

[0052] In response to the confidence level being greater than a preset threshold, the fourth audio is determined as the target audio.

[0053] In another embodiment of the present disclosure, the scene information includes the noise and the audio output mode, and the third determination module configures a first weight for the first similarity according to the scene information, and configures a second weight for the second similarity according to the scene information, specifically for:

[0054] Determining a noise interval in which the noise is located;

[0055] According to the noise interval and the audio output mode, a first weight is configured for the first similarity and a second weight is configured for the second similarity.

[0056] In yet another embodiment of the present disclosure, the third determination module configures a first weight for the first similarity and a second weight for the second similarity according to the noise interval and the audio output mode, specifically for:

[0057] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is an external speaker mode, setting the first weight to be smaller than the second weight;

[0058] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight;

[0059] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0060] In yet another embodiment of the present disclosure, the third determination module configures a first weight for the first similarity and a second weight for the second similarity according to the noise interval and the audio output mode, specifically for:

[0061] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is an external speaker mode, setting the first weight to be equal to the second weight;

[0062] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight;

[0063] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0064] In another embodiment of the present disclosure, the third determination module configures a first weight for the first similarity and a second weight for the second similarity according to the noise interval and the audio output mode, specifically for:

[0065] In response to the noise interval indicating that the environment in which the terminal device is located is a high-noise environment, the first weight is set to be greater than the second weight.

[0066] In yet another embodiment of the present disclosure, the third determination module determines a first similarity between the first audio feature and an internally recorded audio feature of a stored fourth audio, specifically for:

[0067] Acquire a target parameter, where the target parameter includes at least one of a current time period and a location of the terminal device;

[0068] An audio database matching the target parameter is determined, and a fourth audio stored in the audio database is determined.

[0069] In another embodiment of the present disclosure, the second determining module determines the first audio feature of the second audio and the second audio feature of the third audio, specifically for:

[0070] Obtaining a first proportion of human voice and a second proportion of non-human voice in the first audio;

[0071] Determining an audio feature recognition strategy according to the first proportion and the second proportion;

[0072] The audio feature recognition strategy is adopted to recognize the first audio feature of the second audio and the second audio feature of the third audio.

[0073] In another embodiment of the present disclosure, the second determination module determines the audio feature recognition strategy according to the first proportion and the second proportion, specifically for:

[0074] In response to the first proportion being less than a first preset proportion, or the first proportion being less than a second preset proportion and the second proportion being greater than a third preset proportion, determining the recognition mode of song recognition by listening to songs as the audio feature recognition strategy;

[0075] In response to the second proportion being less than the third preset proportion, or the second proportion being not less than the third preset proportion and the first proportion being not less than the second preset proportion, the recognition mode of identifying songs by humming is determined as the audio feature recognition strategy.

[0076] In another embodiment of the present disclosure, the second determination module adopts the audio feature recognition strategy to recognize the first audio feature of the second audio and the second audio feature of the third audio, specifically for:

[0077] In response to the audio feature recognition strategy being a recognition mode of humming to identify a song, the recognition mode of humming to identify a song is used to identify the second audio and the third audio for a preset time length, and then the recognition mode of lyrics to identify a song is used to identify the second audio and the third audio to obtain the first audio feature and the second audio feature.

[0078] In yet another embodiment of the present disclosure, the start playing time point of the second audio is the same as that of the third audio.

[0079] In a fourth aspect of the embodiments of the present disclosure, there is provided a computing device, comprising: at least one processor;

[0080] and a memory communicatively coupled to the at least one processor;

[0081] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the computing device to execute the method described in the first aspect and any one of the embodiments.

[0082] According to the audio recognition method, apparatus, medium and computing device of the embodiment of the present disclosure, the method includes: obtaining the second audio obtained by the terminal device internally recording the first audio and the third audio obtained by the terminal device externally recording the first audio; determining the scene information of the terminal device recording the first audio; determining the first audio feature of the second audio and the second audio feature of the third audio; determining the target audio that matches the first audio based on the scene information, the first audio feature and the second audio feature, and determining the information of the target audio as the identification information of the first audio. In this technical solution, by processing the audio features of the internally recorded and externally recorded audios and using the scene information in a comprehensive manner, higher quality and consistent audio fingerprints are generated, which greatly improves the recognition rate of the song recognition function and brings a better experience to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:

[0084] Figure 1 A schematic diagram of a scenario of an audio recognition method provided by an embodiment of the present disclosure;

[0085] Figure 2 A flowchart of an audio recognition method provided by an embodiment of the present disclosure;

[0086] Figure 3 A flowchart of an audio recognition method provided by another embodiment of the present disclosure;

[0087] Figure 4 A flowchart of an audio recognition method provided by yet another embodiment of the present disclosure;

[0088] Figure 5 A structural diagram of a storage medium provided in one embodiment of the present disclosure;

[0089] Figure 6 A structural diagram of an audio recognition device provided by an embodiment of the present disclosure;

[0090] Figure 7 A structural diagram of a computing device provided for an embodiment of the present disclosure;

[0091] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0092] The principles and spirit of the present disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0093] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0094] According to an embodiment of the present disclosure, a method, medium, apparatus and computing device for audio recognition are proposed.

[0095] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0096] In addition, any number of elements in the drawings is for illustration and not limitation, and any naming is only for distinction and does not have any limiting meaning.

[0097] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure. SUMMARY OF THE INVENTION

[0099] The inventors have found that in the related technologies of song recognition by listening, the identification is often carried out by collecting audio data and extracting audio fingerprints from the audio data, which leads to the following technical problems:

[0100] The effect of song recognition by listening is generally limited by a variety of factors, which may reduce the recognition effect. The simple recognition method in the relevant technology cannot avoid the influence of these factors, resulting in poor recognition effect.

[0101] Based on the above technical problems, the present disclosure provides an audio recognition method, device, medium and computing device, which obtains audio obtained by internal recording and external recording respectively in complex usage scenarios, and optimizes the audio fingerprint in the obtained audio based on scene information, thereby significantly improving the accuracy and reliability of audio recognition and greatly improving the recognition rate of the song recognition function.

[0102] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0103] Application Scenario Overview

[0104] First, the audio recognition method provided by the embodiment of the present disclosure can be applied to Figure 1 The scene diagram shown. Figure 1 A schematic diagram of a scenario of an audio recognition method provided by an embodiment of the present disclosure. Figure 1 As shown, the scenario includes: a computing device 11 and a terminal device 12 .

[0105] In this application scenario, the terminal device 12 collects the audio that the user wants to recognize through internal recording and external recording, and then the computing device 11 obtains the audio corresponding to the internal recording and external recording respectively collected by the terminal device 12, and the scene information corresponding to the terminal device 12, and obtains the recognition result of the recognized audio through the corresponding processing process, that is, it can be the song name, singer, lyrics, etc., and then sends the recognition result to the terminal device to let the user know; wherein, the implementation of the processing process can refer to the schemes of the following embodiments;

[0106] It should be noted that Figure 1 This is only a schematic diagram of an application scenario provided by the embodiment of the present disclosure. Figure 1 does not limit the equipment included in Figure 1 The positional relationship between the devices in the Figure 1 The application scenario shown may also include related devices corresponding to the music library used by the computing device 11 during matching, as well as external devices on the terminal device 12 and the like.

[0107] Exemplary Methods

[0108] Combine the following Figure 1 For application scenarios, refer to Figure 1 To describe the method for audio recognition according to an exemplary embodiment of the present disclosure. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of the present disclosure, and the embodiments of the present disclosure are not limited in this regard. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0109] Figure 2 The flowchart of the audio recognition method provided by an embodiment of the present disclosure is shown in FIG. The method of the embodiment of the present disclosure can be applied to a computing device, which can be a server. Figure 2 As shown, the method of the embodiment of the present disclosure includes:

[0110] S21, obtaining a second audio obtained by internally recording the first audio by the terminal device and a third audio obtained by externally recording the first audio by the terminal device;

[0111] In this step, audio (such as music, crosstalk, etc.) is played on the terminal device, which is recorded as the first audio. At this time, the terminal device records the first audio internally to obtain the second audio, and records the first audio externally to obtain the third audio.

[0112] Afterwards, the computing device acquires the second audio and the third audio.

[0113] Among them, the terminal device can be a mobile phone, computer, tablet, etc.

[0114] In a possible implementation, internal recording refers to recording sound using a microphone or audio input interface inside the terminal device, or the sound played in the computer is recorded directly inside the computer without being played through a speaker; external recording refers to recording sound by connecting an external microphone or other audio source to the terminal device, or the sound inside the computer is played through a speaker and then recorded using other recording equipment.

[0115] That is, optionally, before the implementation of this step, the following step may be performed: the terminal device detects whether the first audio is currently being played and whether it is in an external speaker state, and if both are the case, recording is performed.

[0116] Optionally, the start playing time point of the second audio and the third audio is the same.

[0117] In this implementation, since there are two different recording methods for recognizing the first audio, during recording, in order to make the recognition result of the first audio accurate, it is necessary to ensure that the starting playback time points of the first audio and the second audio are the same, that is, the externally recorded third audio and the internally recorded second audio are time synchronized.

[0118] Specific possible implementations could be:

[0119] 1. When starting internal and external audio collection, the terminal device records their respective start timestamps at the same time. When the computing device obtains the above audio data, the recorded timestamps can be used to preliminarily align the audio data corresponding to the internal and external recordings, and the audio signals can be synchronized and corrected by the cross-correlation method;

[0120] 2. The terminal device can also compensate for its own delay based on the recorded timestamp to ensure that the starting points of the two audio sources are consistent, and then send it to the computing device.

[0121] S22, determining scene information of the terminal device recording the first audio;

[0122] The scene information includes at least one of a device type of the terminal device, noise in an environment where the terminal device is located, and an audio output mode of the terminal device playing the first audio;

[0123] In this step, the terminal device can obtain the scene in which the first audio is recorded, and then send relevant information of the scene to the computing device, so that the computing device can determine the scene information based on the information.

[0124] Optionally, the scenario may include at least one of the following:

[0125] 1. Noise: The terminal device can use a microphone to detect the intensity of external environmental noise;

[0126] 2. Device type, which may include but is not limited to mobile phones, tablets, computers, etc.;

[0127] 3. Audio output modes may include but are not limited to external speakers, headphones, car speakers, Bluetooth speakers, etc.

[0128] In addition, there may be time and geographic location information when the audio was collected.

[0129] That is, in a specific possible implementation, the client corresponding to functions such as song recognition in the terminal device captures ambient noise through a microphone, calculates the noise intensity, detects the current terminal device type and audio output mode, and uploads the client to the corresponding server, that is, the computing device disclosed in the present invention.

[0130] In one possible implementation, the client reports that the noise is low noise, the device type is a mobile phone, and the audio output mode is external speaker, then the computing device can determine that the scene information for recording the first audio is that the noise is low noise, the device type is a mobile phone, and the audio output mode is external speaker.

[0131] S23, determining a first audio feature of the second audio and a second audio feature of the third audio;

[0132] In this step, after obtaining the second audio corresponding to the internal recording and the third audio corresponding to the external recording, audio features of the second audio and the third audio are extracted respectively, that is, audio fingerprints corresponding to the corresponding audios can be obtained.

[0133] Among them, the audio fingerprint is a digital signature used for audio recognition, which can uniquely identify a song or audio clip.

[0134] S24. Determine a target audio that matches the first audio according to the scene information, the first audio feature, and the second audio feature, and determine the information of the target audio as identification information of the first audio.

[0135] In this step, in order to achieve accurate recognition, after obtaining the first audio feature and the second audio feature, it is necessary to process the first audio feature and the second audio feature accordingly based on the scene information that affects the recognition accuracy to obtain the target audio that matches the first audio.

[0136] The target audio information may include the name, author, download link, and other information of the audio.

[0137] In one possible implementation, after matching the audio features of the internal and external recordings respectively, two sets of matching results will be obtained. Each set of matching results will contain one or more candidate songs and their corresponding confidence levels. In order to combine the two sets of results, weights are assigned to the two sets of results according to different usage scenarios (device type, audio output mode, ambient noise level), and then weighted processing is performed to obtain the final recognition result. The process in this embodiment can be referred to as follows Figure 3 The embodiment shown.

[0138] The embodiments of the present disclosure provide an audio recognition method, including: obtaining a second audio obtained by internally recording a first audio by a terminal device and a third audio obtained by externally recording the first audio by the terminal device; determining the scene information of the first audio recorded by the terminal device; determining the first audio feature of the second audio and the second audio feature of the third audio; determining a target audio that matches the first audio based on the scene information, the first audio feature and the second audio feature, and determining the information of the target audio as the identification information of the first audio. By processing the audio features of the internally recorded and externally recorded audios and using the scene information in a comprehensive manner, a higher quality and more consistent audio fingerprint is generated, which greatly improves the recognition rate of the song recognition function and brings a better experience to users.

[0139] based on Figure 2 In the embodiment shown, S24 will be further described below. Figure 3 FIG. 1 is a flowchart of an audio recognition method provided by another embodiment of the present disclosure. Figure 3 As shown, S24 may include the following steps:

[0140] S31, determining a first similarity between the first audio feature and the stored internally recorded audio feature of the fourth audio, and determining a second similarity between the second audio feature and the externally recorded audio fingerprint of the fourth audio;

[0141] In this step, a plurality of audio features are pre-stored in the computing device, and these audio features can be determined based on an audio database (such as a music library).

[0142] For example, the database is divided into popular database and full database according to the recent popularity, the database is divided into high-quality database and low-quality database according to the audio quality, and the database is divided into English database and Russian database according to the language. The computing device will reasonably allocate server resources according to the current audio information. For example, the collection effect between 21:00 and 2:00 in the morning is obviously better than that in other time periods, and the collection effect on weekends is obviously better than that on weekdays. In this time period, the popular database and high-quality database will be requested first; or the corresponding language database will be requested first according to the user's geographic location and the recognized language information.

[0143] That is, in this embodiment, for the first audio feature, at least one fourth audio having a higher similarity with the first audio feature can be determined, that is, the first similarity can be known; and the similarity between the at least one fourth audio and the second audio feature can be determined, that is, the second similarity can be known.

[0144] Optionally, the manner of determining the first similarity between the first audio feature and the stored internally recorded audio feature of the fourth audio in S31 may be:

[0145] Step 1: Obtain target parameters, where the target parameters include at least one of a current time period and a location of the terminal device;

[0146] For example, if the target parameter is the current time period, it can be considered as the preset time period in which the current time period is located. For example, the collection effect between 21:00 and 2:00 in the morning every day is obviously better than other time periods. For example, the collection effect on weekends is obviously better than that on weekdays. In this case, the current time period will give priority to requesting the audio databases corresponding to the popular library and the high-quality library.

[0147] For another example, the target parameter is the location of the terminal device, such as the Chinese-speaking area, and the audio database corresponding to the target parameter is the Chinese audio database.

[0148] In addition, the usage area can also be an area characterized by local dialects, and there is also an audio database corresponding to the local dialect.

[0149] Step 2: determine an audio database that matches the target parameter, and determine the fourth audio stored in the audio database.

[0150] In this implementation, in order to improve the accuracy of recognition, after the audio database matching the target parameters is determined, the fourth audio stored in the audio database is determined.

[0151] S32, configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information;

[0152] In this step, corresponding weights may be assigned to the first and second similarities by adopting a set weight assignment strategy based on received scene information, such as environmental noise information, device type, and audio output mode information.

[0153] Optionally, the scene information includes noise and audio output mode, then:

[0154] In a possible implementation, S32 may be implemented by the following steps:

[0155] Step 1: Determine the noise interval where the noise is located;

[0156] In this implementation, the noise interval in which the noise is located may be determined based on the intensity of the noise.

[0157] The noise range may be low noise (eg, less than 40 dB), medium noise (eg, between 40 dB and 70 dB), and high noise (eg, greater than 70 dB).

[0158] Step 2: According to the noise interval and the audio output mode, a first weight is configured for the first similarity and a second weight is configured for the second similarity.

[0159] Exemplarily, the noise range in step 2 can be divided into low noise, medium noise, and high noise. Accordingly, for different noise ranges, there are the following possible implementations:

[0160] Type 1: When the noise interval indicates that the environment in which the terminal device is located is a low-noise environment:

[0161] When the audio output mode is the external speaker mode, the first weight is set to be smaller than the second weight; when the audio output mode is the headphone mode, the first weight is set to be greater than the second weight; when the audio output mode is the Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0162] For example, in external speaker mode, the weight of externally recorded audio fingerprint is higher, and the weight of internally recorded audio fingerprint is lower; in headphone mode, the weight of internally recorded audio fingerprint is higher, and the weight of externally recorded audio fingerprint is lower; in Bluetooth speaker mode, the weights of externally recorded audio fingerprint and internally recorded audio fingerprint are equivalent.

[0163] Type 2: When the noise interval indicates that the environment in which the terminal device is located is a medium noise environment:

[0164] When the audio output mode is the external speaker mode, the first weight is set to be equal to the second weight; when the audio output mode is the headphone mode, the first weight is set to be greater than the second weight; when the audio output mode is the Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0165] For example, in external speaker mode, the weights of externally recorded audio fingerprint and internally recorded audio fingerprint are equal; in headphone mode, the weight of internally recorded audio fingerprint is higher and the weight of externally recorded audio fingerprint is lower; in Bluetooth speaker mode, the weights of externally recorded audio fingerprint and internally recorded audio fingerprint are equal.

[0166] Type 3: When the noise interval indicates that the terminal device is in a high-noise environment:

[0167] When the audio output mode is external speaker mode, the first weight is set to be less than the second weight; when the audio output mode is headphone mode, the first weight is set to be greater than the second weight; when the audio output mode is Bluetooth speaker mode, the first weight is set to be greater than and equal to the second weight.

[0168] For example, in external speaker mode, the weight of the externally recorded audio fingerprint is not less than that of the internally recorded audio fingerprint; in headphone mode, the weight of the internally recorded audio fingerprint is higher and the weight of the externally recorded audio fingerprint is lower; in Bluetooth speaker mode, the weights of the externally recorded audio fingerprint and the internally recorded audio fingerprint are equivalent.

[0169] S33, determining a confidence level between the first audio and the fourth audio according to the first similarity, the first weight, the second similarity, and the second weight;

[0170] In this implementation, taking the fourth audio as song A as an example, the confidence of song A can be the first similarity*first weight+second similarity*second weight.

[0171] For example, if the first similarity is 0.85, the first weight is 0.3, the second similarity is 0.80, and the second weight is 0.7, then the confidence between the first audio and song A is:

[0172] 0.85*0.3+0.80*0.7=0.815

[0173] S34: In response to the confidence level being greater than a preset threshold, determine the fourth audio as the target audio.

[0174] In this step, in order to ensure the accuracy of the recommendation, a preset threshold needs to be set to filter the confidence between the possible first audio and the fourth audio, and it is considered that the fourth audio with a larger confidence is likely to be the target audio.

[0175] For example, the preset threshold is 0.2.

[0176] It should be understood that there may be at least one fourth audio. In the subsequent output process, at least the first and fourth audios with confidence levels greater than a preset threshold can be determined as target audios, and the target audios can be fed back to the client. They can be displayed in order of confidence levels.

[0177] For example, the confidence of song A is 0.815, the confidence of song B is 0.49, and the confidence of song C is 0.225. Then the song list [A, C, B] is sorted from high to low according to the confidence and returned to the client to be presented to the user.

[0178] The embodiment of the present disclosure provides an audio recognition method, including: determining a first similarity between a first audio feature and a stored internally recorded audio feature of a fourth audio, and determining a second similarity between a second audio feature and an externally recorded audio fingerprint of the fourth audio; configuring a first weight for the first similarity according to scene information, and configuring a second weight for the second similarity according to scene information; determining the confidence between the first audio and the fourth audio according to the first similarity, the first weight, the second similarity, and the second weight; in response to the confidence being greater than a preset threshold, determining the fourth audio as the target audio. In this technical solution, after matching the internally recorded and externally recorded audio fingerprints respectively, two matching results will be obtained, and each matching result will contain one or more candidate songs and their corresponding confidences. In order to combine the two sets of results, weights are assigned to the two results according to different usage scenarios (device type, audio output mode, ambient noise level), and then weighted processing is performed, which can greatly improve the accuracy of recognition.

[0179] Based on the above embodiment, S23 is further described below. Figure 4 FIG. 1 is a flowchart of an audio recognition method provided by another embodiment of the present disclosure. Figure 4 As shown, S23 may include the following steps:

[0180] S41, obtaining a first proportion of human voices and a second proportion of non-human voices in the first audio;

[0181] After this step, there may be several different recognition modes for the proportion of non-human voice (such as music proportion) and the proportion of human voice. Therefore, in this step, it is necessary to first obtain the first proportion of human voice and the second proportion of non-human voice in the first audio.

[0182] In a possible implementation, the volume data such as the average volume or loudness of human voice and non-human voice can be measured respectively, and then the ratio of human voice to non-human voice is calculated based on the measured volume data, that is, the first proportion of human voice and the second proportion of non-human voice are obtained.

[0183] It should be understood that since the volume of human voices and non-human voices may change dynamically, the measurement results at multiple time points may be considered and processed comprehensively.

[0184] S42, determining an audio feature recognition strategy according to the first proportion and the second proportion;

[0185] In this step, different audio feature recognition strategies may be adopted according to the difference between the first proportion and the second proportion of non-human voices. For example, they may include: song recognition by listening mode, song recognition by humming mode, and song recognition by lyrics mode (see the embodiment in S43).

[0186] Optionally, S42 may have the following possible implementations:

[0187] The first method is to determine the recognition mode of song recognition by listening to songs as the audio feature recognition strategy in response to the first proportion being less than the first preset proportion, or the first proportion being less than the second preset proportion and the second proportion being greater than the third preset proportion;

[0188] In this implementation, for example, the first preset proportion may be set to 6%, the second preset proportion may be set to 70%, and the third preset proportion may be set to 3%;

[0189] That is, the proportion of vocals is analyzed first. When the proportion of vocals is lower than 6%, the song recognition mode is used. Secondly, the proportion of music is analyzed. When the proportion of music is higher than 3% and when the proportion of vocals does not exceed 70%, the song recognition mode is used.

[0190] The second method is to determine the recognition mode of song recognition by humming as the audio feature recognition strategy in response to the second proportion being less than the third preset proportion, or the second proportion being not less than the third preset proportion and the first proportion being not less than the second preset proportion;

[0191] That is, in analyzing the proportion of music, when the proportion of music is less than 3%, the humming song recognition mode is used first; when the proportion of music is higher than 3% and the proportion of human voice exceeds 70%, the humming song recognition mode is used first.

[0192] S43: Use an audio feature recognition strategy to recognize a first audio feature of the second audio and a second audio feature of the third audio.

[0193] In this step, the audio feature recognition strategy is determined above, and the first audio feature and the second audio feature obtained above are recognized based on the corresponding recognition mode.

[0194] That is, the implementation mainly extracts representative features from each frame of audio signal, and constructs a unique and stable audio fingerprint based on the extracted features. That is, as an audio feature, the audio fingerprint is a digital signature for audio recognition, which can uniquely identify a song or audio clip.

[0195] Optionally, S43 may have the following possible implementations:

[0196] In response to the audio feature recognition strategy being a recognition mode of identifying songs by humming, the recognition mode of identifying songs by humming is used to identify the second audio and the third audio for a preset time length, and then the recognition mode of identifying songs by lyrics is used to obtain the first audio feature and the second audio feature.

[0197] In this implementation, for example, the preset time length may be set to 10 seconds.

[0198] That is, when the humming mode has no result at the 10th second, the lyrics recognition mode is used to obtain the first audio feature and the second audio feature for the second audio and the third audio.

[0199] The embodiment of the present disclosure provides an audio recognition method, including: obtaining a first proportion of human voice and a second proportion of non-human voice in a first audio; determining an audio feature recognition strategy according to the first proportion and the second proportion; using the audio feature recognition strategy to recognize a first audio feature of the second audio and a second audio feature of the third audio. In this technical solution, different song recognition modes are divided according to the proportion of non-human voice and the proportion of human voice, so as to configure different recognition strategies, and the recognition accuracy is improved when the recognition direction is refined.

[0200] Exemplary Media

[0201] After introducing the method of the exemplary embodiment of the present disclosure, next, refer to Figure 5 A storage medium according to an exemplary embodiment of the present disclosure is described.

[0202] refer to Figure 5 As shown, the storage medium 50 stores a program product for implementing the above method according to an embodiment of the present disclosure, which can be a portable compact disk read-only memory (CD-ROM) and includes program code, and can be run on a computing device, such as a server. However, the program product of the present disclosure is not limited thereto.

[0203] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0204] The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable signal medium may also be any readable medium other than a readable storage medium.

[0205] Program code for performing the disclosed operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0206] Exemplary Devices

[0207] After introducing the medium of the exemplary embodiment of the present disclosure, next, the audio recognition device of the exemplary embodiment of the present disclosure is described with reference to the figure, which is used to implement the method in any of the above method embodiments. The implementation principle and technical effect are similar and will not be repeated here.

[0208] Figure 6 This is a structural diagram of an audio recognition device provided by an embodiment of the present disclosure. Figure 6 As shown, the audio recognition device includes:

[0209] An acquisition module 61 is used to acquire a second audio obtained by internally recording the first audio and a third audio obtained by externally recording the first audio;

[0210] A first determining module 62, configured to determine scenario information of a terminal device recording a first audio, wherein the scenario information includes at least one of a device type of the terminal device, noise in an environment where the terminal device is located, and an audio output mode of the terminal device playing the first audio;

[0211] A second determining module 63, configured to determine a first audio feature of a second audio and a second audio feature of a third audio;

[0212] The third determination module 64 is used to determine the target audio that matches the first audio according to the scene information, the first audio feature and the second audio feature, and determine the information of the target audio as the identification information of the first audio.

[0213] In one embodiment of the present disclosure, the third determination module 64 determines the target audio that matches the first audio according to the scene information, the first audio feature, and the second audio feature, specifically for:

[0214] Determine a first similarity between the first audio feature and a stored internally recorded audio feature of a fourth audio, and determine a second similarity between the second audio feature and an externally recorded audio fingerprint of the fourth audio;

[0215] configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information;

[0216] Determining a confidence level between the first audio and the fourth audio according to the first similarity, the first weight, the second similarity, and the second weight;

[0217] In response to the confidence level being greater than a preset threshold, the fourth audio is determined as the target audio.

[0218] In another embodiment of the present disclosure, the scene information includes noise and an audio output mode, and the third determination module 64 configures a first weight for the first similarity according to the scene information, and configures a second weight for the second similarity according to the scene information, specifically for:

[0219] Determine the noise interval in which the noise is located;

[0220] According to the noise interval and the audio output mode, a first weight is configured for the first similarity and a second weight is configured for the second similarity.

[0221] In another embodiment of the present disclosure, the third determination module 64 configures a first weight for the first similarity and a second weight for the second similarity according to the noise interval and the audio output mode, specifically for:

[0222] In response to the noise interval indicating that the environment in which the terminal device is located is a low noise environment and the audio output mode is an external speaker mode, setting the first weight to be smaller than the second weight;

[0223] In response to the noise interval indicating that the environment in which the terminal device is located is a low noise environment and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight;

[0224] In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0225] In yet another embodiment of the present disclosure, the third determination module 64 configures a first weight for the first similarity and a second weight for the second similarity according to the noise interval and the audio output mode, specifically for:

[0226] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment and the audio output mode is an external speaker mode, setting the first weight to be equal to the second weight;

[0227] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight;

[0228] In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

[0229] In another embodiment of the present disclosure, the third determination module 64 configures a first weight for the first similarity and a second weight for the second similarity according to the noise interval and the audio output mode, specifically for:

[0230] In response to the noise interval indicating that the environment in which the terminal device is located is a high noise environment, the first weight is set to be greater than the second weight.

[0231] In another embodiment of the present disclosure, the third determination module 64 determines a first similarity between the first audio feature and the internally recorded audio feature of the stored fourth audio, specifically for:

[0232] Acquire a target parameter, where the target parameter includes at least one of a current time period and a location of the terminal device;

[0233] An audio database matching the target parameter is determined, and a fourth audio stored in the audio database is determined.

[0234] In another embodiment of the present disclosure, the second determination module 63 determines the first audio feature of the second audio and the second audio feature of the third audio, specifically for:

[0235] Obtain a first proportion of human voice and a second proportion of non-human voice in the first audio;

[0236] Determine an audio feature recognition strategy according to the first proportion and the second proportion;

[0237] An audio feature recognition strategy is adopted to recognize the first audio feature of the second audio and the second audio feature of the third audio.

[0238] In another embodiment of the present disclosure, the second determination module 63 determines the audio feature recognition strategy according to the first proportion and the second proportion, specifically for:

[0239] In response to the first proportion being less than the first preset proportion, or the first proportion being less than the second preset proportion and the second proportion being greater than the third preset proportion, determining the recognition mode of song recognition by listening to the song as the audio feature recognition strategy;

[0240] In response to the second proportion being less than the third preset proportion, or the second proportion being not less than the third preset proportion and the first proportion being not less than the second preset proportion, the recognition mode of identifying songs by humming is determined as the audio feature recognition strategy.

[0241] In another embodiment of the present disclosure, the second determination module 63 adopts an audio feature recognition strategy to recognize the first audio feature of the second audio and the second audio feature of the third audio, specifically for:

[0242] In response to the audio feature recognition strategy being a recognition mode of humming to identify a song, the recognition mode of humming to identify a song is used to identify the second audio and the third audio for a preset time length, and then the recognition mode of lyrics to identify a song is used to identify the second audio and the third audio to obtain the first audio feature and the second audio feature.

[0243] In yet another embodiment of the present disclosure, the start playing time point of the second audio and the third audio is the same.

[0244] The audio recognition device provided in the embodiment of the present disclosure can be used to execute the audio recognition method in any of the above embodiments. The implementation principle and technical effect are similar and will not be repeated here.

[0245] Exemplary Computing Devices

[0246] After introducing the method, medium and apparatus of the exemplary embodiments of the present disclosure, next, reference is made to Figure 7 A computing device according to an exemplary embodiment of the present disclosure is described.

[0247] Figure 7 The computing device shown (which may be the computing device 11 described above) is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0248] like Figure 7 As shown, the computing device is in the form of a general computing device. The components of the computing device may include, but are not limited to: at least one processing unit 71, at least one storage unit 72, and a bus 73 connecting different system components (including the processing unit 71 and the storage unit 72). Among them, at least one storage unit 72 stores computer-executable instructions; at least one processing unit 71 includes a processor, and the processor executes the computer-executable instructions to implement the method described above.

[0249] The bus 73 includes a data bus, a control bus, and an address bus.

[0250] The storage unit 72 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 721 and / or a cache memory 722 , and may further include a readable medium in the form of a non-volatile memory, such as a read-only memory (ROM) 723 .

[0251] The storage unit 72 may also include a program / utility 725 having a set (at least one) of program modules 727, such program modules 727 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0252] The computing device may also communicate with one or more external devices 77 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via input / output (I / O) interface 75. In addition, the computing device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks such as the Internet) via network adapter 76. Figure 7 As shown, network adapter 76 communicates with other modules of the computing device via bus 73. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the computing device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0253] It should be noted that although several units / modules or sub-units / modules of the audio recognition device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for embodiment.

[0254] In addition, although the operations of the disclosed method are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0255] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims.

Claims

1. An audio recognition method, It is characterized in that include: Acquire a second audio obtained by internally recording the first audio by a terminal device and a third audio obtained by externally recording the first audio by the terminal device; Determine scenario information in which the terminal device records the first audio, where the scenario information includes at least one of a device type of the terminal device, noise in an environment in which the terminal device is located, and an audio output mode in which the terminal device plays the first audio; determining a first audio characteristic of the second audio and a second audio characteristic of the third audio; According to the scene information, the first audio feature, and the second audio feature, a target audio that matches the first audio is determined, and information of the target audio is determined as identification information of the first audio.

2. The audio recognition method according to claim 1, It is characterized in that The determining, according to the scene information, the first audio feature, and the second audio feature, a target audio that matches the first audio includes: Determine a first similarity between the first audio feature and a stored internally recorded audio feature of a fourth audio, and determine a second similarity between the second audio feature and an externally recorded audio fingerprint of the fourth audio; configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information; Determine a confidence level between the first audio and the fourth audio according to the first similarity, the first weight, the second similarity, and the second weight; In response to the confidence level being greater than a preset threshold, the fourth audio is determined as the target audio.

3. The audio recognition method according to claim 2, It is characterized in that The scene information includes the noise and the audio output mode, and configuring a first weight for the first similarity according to the scene information, and configuring a second weight for the second similarity according to the scene information, includes: Determining a noise interval in which the noise is located; According to the noise interval and the audio output mode, a first weight is configured for the first similarity and a second weight is configured for the second similarity.

4. The audio recognition method according to claim 3, It is characterized in that The configuring a first weight for the first similarity and configuring a second weight for the second similarity according to the noise interval and the audio output mode includes: In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is an external speaker mode, setting the first weight to be smaller than the second weight; In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight; In response to the noise interval indicating that the environment in which the terminal device is located is a low-noise environment, and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

5. The audio recognition method according to claim 3, It is characterized in that The configuring a first weight for the first similarity and configuring a second weight for the second similarity according to the noise interval and the audio output mode includes: In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is an external speaker mode, setting the first weight to be equal to the second weight; In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is a headphone mode, setting the first weight to be greater than the second weight; In response to the noise interval indicating that the environment in which the terminal device is located is a medium noise environment, and the audio output mode is a Bluetooth speaker mode, the first weight is set to be equal to the second weight.

6. The audio recognition method according to claim 3, It is characterized in that The configuring a first weight for the first similarity and configuring a second weight for the second similarity according to the noise interval and the audio output mode includes: In response to the noise interval indicating that the environment in which the terminal device is located is a high-noise environment, the first weight is set to be greater than the second weight.

7. The audio recognition method according to claim 2, It is characterized in that The determining a first similarity between the first audio feature and a stored internally recorded audio feature of a fourth audio comprises: Acquire a target parameter, where the target parameter includes at least one of a current time period and a location of the terminal device; An audio database matching the target parameter is determined, and a fourth audio stored in the audio database is determined.

8. An audio recognition device, It is characterized in that include: An acquisition module, used to acquire a second audio obtained by internally recording the first audio by a terminal device and a third audio obtained by externally recording the first audio by the terminal device; A first determining module is used to determine scenario information of the terminal device recording the first audio, where the scenario information includes at least one of a device type of the terminal device, noise in an environment where the terminal device is located, and an audio output mode of the terminal device playing the first audio; A second determining module, configured to determine a first audio feature of the second audio and a second audio feature of the third audio; The third determination module is used to determine the target audio that matches the first audio according to the scene information, the first audio feature and the second audio feature, and determine the information of the target audio as the identification information of the first audio.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method according to any one of claims 1 to 7 is implemented.

10. A computing device, It is characterized in that include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the computing device to perform the method as claimed in any one of claims 1 to 7.

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