Volume unit table display optimization method and system, storage medium and equipment

Abstract

The invention relates to the technical field of audio signal processing, and discloses a volume unit table display optimization method and system, a storage medium and equipment, and the method comprises the steps: collecting an audio input signal in real time, and carrying out the preprocessing of the collected audio input signal, so as to obtain audio data suitable for analysis; extracting volume characteristics capable of reflecting audio signal strength and dynamic characteristics based on the preprocessed audio data; recognizing a current volume mode according to the extracted volume features, wherein the volume mode comprises a low volume mode, a medium volume mode and a high volume mode; dynamically calculating display parameters of a volume unit table VU Meter according to the identified volume mode, wherein the display parameters at least comprise a display range, a scale interval and a response time constant; and finally, the visual display effect of the volume unit table VU Meter is updated based on the calculated display parameters. According to the method, the volume unit table VU Meter can provide accurate and visual volume visual feedback in various volume scenes.

Description

Technical Field

[0001] This invention relates to the field of audio signal processing technology, and in particular to a method, system, storage medium, and device for optimizing the display of volume unit tables. Background Technology

[0002] Volume Unit Meters (VU Meters) are indispensable monitoring tools in the audio field, widely used in professional recording, broadcasting, live sound reinforcement, and consumer audio equipment. Their core function is to intuitively display the level strength of audio signals, assisting users in precise level management and preventing signal overload or excessively low levels.

[0003] Traditional volume unit meters generally use a fixed display range and scale distribution, typically with 0dB as the reference point and a preset static display range (e.g., -20dB to +3dB). While this design is acceptable under standard operating conditions, its inherent flaws become prominent in complex real-world audio applications. First, the fixed display mode cannot adapt to wide dynamic range volume changes. When processing low-level signals, such as ambient sound acquisition or long-distance sound pickup, the pointer or level bar remains at the bottom of the dial for extended periods, making it difficult for users to perceive subtle level fluctuations and hindering precise gain adjustment. Conversely, when processing continuous high-level signals, such as in modern pop music, the pointer frequently touches the top, making it impossible for users to distinguish between different intensities of high levels and even masking transient clipping distortion.

[0004] Secondly, traditional volume unit tables struggle to handle the varying characteristics of different types of audio content. Classical music and other acoustic recordings typically have a wide dynamic range exceeding 60dB, while highly compressed speech and pop music may have a dynamic range of less than 15dB. Volume unit tables with fixed response characteristics cannot simultaneously meet these two drastically different monitoring needs: they may lose detail with wide dynamic range content and appear sluggish with narrow dynamic range content.

[0005] While existing technologies offer solutions for manually adjusting the sensitivity or display range of volume units, this requires users to have professional knowledge and frequent intervention, which not only increases operational complexity but is also often not timely or accurate enough when dealing with rapidly changing audio content.

[0006] In summary, traditional fixed-display volume unit tables have significant shortcomings in terms of accuracy, applicability, and user experience. The audio field urgently needs a solution that can intelligently sense audio characteristics and automatically adjust display parameters to meet the growing demand for precise monitoring in modern audio applications. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of the prior art by providing an adaptive volume unit display method. This method intelligently identifies volume modes by analyzing the volume characteristics and dynamic range of audio signals in real time, and dynamically optimizes the display range, scale, and response speed accordingly. This provides accurate and intuitive volume visualization feedback in various volume scenarios.

[0008] On the one hand, a method for displaying a volume unit table is provided, including the following steps: S1: Real-time acquisition of audio input signals, and preprocessing of the acquired audio input signals to obtain audio data suitable for analysis; S2: Extract volume features that reflect the intensity and dynamic characteristics of audio signals based on preprocessed audio data; S3: Identify the current volume mode based on the extracted volume features. The volume mode includes low volume mode, medium volume mode, and high volume mode. S4: Dynamically calculate the display parameters of the volume unit table based on the identified volume mode. The display parameters include at least the display range, scale interval, and response time constant. S5: Update the visual display of the volume unit table based on the calculated display parameters.

[0009] Further, in step S2, the extraction of the volume features includes: For the preprocessed audio data, the instantaneous volume value is obtained by calculating the root mean square (RMS) value of each frame of audio signal and converting it to decibels. Within a preset duration sliding window, the distribution characteristics of volume values ​​are statistically analyzed, including average volume, peak volume, minimum volume, and dynamic range. Simultaneously analyze the rate of change of volume and the frequency of change of volume to capture the dynamic behavior trends of audio signals.

[0010] Preferably, the extraction of the volume feature further includes: The preprocessed audio data is subjected to Fast Fourier Transform (FFT) to analyze its frequency distribution and extract spectral features including the proportion of low-frequency energy, mid-frequency energy, and high-frequency energy. Calculate the spectral centroid and spectral bandwidth parameters to help identify the type and characteristics of audio content.

[0011] Further, in step S3, the identification of the volume mode includes: Based on the extracted average volume value, the volume mode is divided into low volume mode, medium volume mode and high volume mode according to a preset threshold; Each volume mode is further subdivided into low dynamic range mode and high dynamic range mode based on the calculated dynamic range; A state machine model is used to manage the switching between different modes, and the stability of mode switching is ensured by setting mode switching thresholds and delay times.

[0012] Preferably, the volume mode recognition further includes: We introduce an audio content type recognition based on a machine learning model to classify audio content into categories such as speech, music, or ambient sound. By combining the audio content type and volume distribution statistics, the initially identified volume patterns are corrected and optimized to generate a display strategy that better matches the audio characteristics.

[0013] Further, in step S4, the display parameters of the dynamically calculated volume unit table include: The display range of the volume unit table is adaptively adjusted according to the identified volume mode. In low volume mode, the display range is reduced to enhance the display accuracy of the low volume area, and in high volume mode, the display range is expanded to avoid display elements touching the top. Simultaneously, the density of the scale interval is adjusted according to the current mode, and different response time constants, including startup time and release time, are configured in low dynamic mode and high dynamic mode respectively.

[0014] Preferably, the display parameters of the dynamically calculated volume unit table further include: Define a color mapping scheme associated with volume levels, assign different colors to low volume, medium volume, high volume and over-limit areas, and dynamically adjust the transition points between colors; It also implements a peak hold function, displaying the instantaneous peak volume in a specific visual form, and adjusting the hold time of the peak indicator according to the current dynamic mode.

[0015] Furthermore, in step S5, the visual display effect of updating the volume unit table includes: Based on dynamically calculated display parameters, update the visual elements of the volume unit table, which may take one or more forms, including pointer type, LED level bar or digital waveform display; When updating the display, a smooth interpolation algorithm is applied to make the movement of the pointer or level bar present a smooth animation effect; When a volume mode switch is detected, a smooth visual transition is performed on the scale range, color mapping, and display position within a preset transition time.

[0016] Furthermore, the method also includes interaction and personalization: The user interface receives manual adjustment commands from the user for the parameters displayed in the volume unit table; Record user adjustment behaviors and preference settings and generate user profiles; Based on the user profile and real-time analysis of audio content characteristics, machine learning algorithms are used to learn user habits, thereby achieving personalized volume unit table display optimization.

[0017] Preferably, the personalized volume unit table display optimization further includes: It provides a scene preset function, allowing users to create and save custom volume unit table configurations for different audio working scenarios; The system automatically detects the current work scenario and loads the corresponding preset configuration; Simultaneously, user feedback data on the adaptive display effect is collected, and online learning algorithms are used to continuously optimize the strategies for volume pattern recognition and display parameter calculation.

[0018] Furthermore, the method also includes system performance optimization and resource management: To optimize system performance, a multi-threaded or asynchronous processing architecture is adopted, and audio acquisition, feature analysis, pattern recognition, and visual rendering tasks are assigned to different execution threads. The allocation of computing resources is dynamically adjusted according to the real-time load of the system, reducing the complexity or frequency of feature analysis under high load and improving analysis accuracy and display refresh rate under low load. Utilize caching mechanisms and lookup tables to accelerate repetitive computational operations.

[0019] Furthermore, the method also includes multi-scenario application extensions: The adaptive volume unit table display method is applied to a multi-channel audio system, whereby the volume characteristics and display parameters of each independent channel are calculated separately, and the volume unit tables of multiple channels can be displayed simultaneously or synthesized in a single display interface. Make the system compatible with international loudness standards and support switching between traditional volume display and loudness display based on the audio loudness unit LUFS; The volume unit display technology is extended to virtual reality (VR) or augmented reality (AR) environments to achieve immersive audio level visualization rendering in three-dimensional space.

[0020] On the other hand, a volume unit table display optimization system is provided, including: The signal acquisition and preprocessing module is used to acquire audio input signals in real time and preprocess the acquired audio input signals to obtain audio data suitable for analysis. The feature extraction module is used to extract volume features that reflect the intensity and dynamic characteristics of the audio signal based on the preprocessed audio data. The pattern recognition module is used to identify the current volume mode based on the extracted volume features. The volume modes include low volume mode, medium volume mode and high volume mode. The dynamic display control module is used to dynamically calculate the display parameters of the volume unit table based on the identified volume mode. The display parameters include at least the display range, scale interval, and response time constant. The visual rendering module is used to update the visual display effect of the volume unit table based on the calculated display parameters.

[0021] In addition, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the volume unit table display optimization method described in any of the preceding claims.

[0022] Meanwhile, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the volume unit table display optimization method described in any of the preceding claims.

[0023] Compared with the prior art, the beneficial effects of the present invention are: This invention effectively overcomes the inherent defects of traditional fixed display modes, such as difficulty in observing subtle changes at low volumes and easy distortion at high volumes, by dynamically adjusting the display range and scale interval of the volume unit table according to the real-time identified volume mode. This allows for accurate and clear visual feedback in any volume scenario. Based on the real-time extraction and analysis of the volume characteristics of audio signals, the system can automatically identify the current volume mode and intelligently calculate and optimize the display parameters accordingly, reducing the reliance on manual user intervention and significantly improving the automation level and operational efficiency of audio monitoring. This invention uses dynamic calculation of response time constant and combined with a smooth visual update algorithm to make the movement of the pointer or level bar of the volume unit meter smoother and more natural, and can more realistically reflect the dynamic characteristics of the audio signal, thereby providing users with a more intuitive and comfortable visual interactive experience. The core steps of this invention do not depend on specific hardware or absolute volume values. Through adaptive signal processing and display control, it can flexibly cope with various complex audio scenarios from low to high volume and from low to high dynamic range, and has good universality and broad application prospects. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for optimizing the display of volume unit tables according to the present invention; Figure 2This is a block diagram of a volume unit table display optimization system according to the present invention; Figure 3 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] This invention acquires and preprocesses audio signals in real time, extracting their volume characteristics and dynamic features. It intelligently identifies different volume modes (low, medium, high) and dynamically optimizes the display range, scale intervals, and response time parameters of the volume unit table accordingly, ultimately achieving accurate updates to the visual display. This method effectively solves the problem of inaccurate display of traditional volume unit tables at extreme volume levels, significantly improving the intuitiveness and cross-scenario applicability of audio monitoring.

[0027] The specific embodiments of the present invention will be described below with reference to the accompanying drawings and examples.

[0028] Example 1 Please see Figure 1 The technical solution for optimizing the display of a volume unit table (VU Meter, hereinafter the same) provided in this embodiment includes the following steps: S1: Real-time acquisition of audio input signals, and preprocessing of the acquired audio input signals to obtain audio data suitable for analysis; S2: Extract volume features that reflect the intensity and dynamic characteristics of audio signals based on preprocessed audio data; S3: Identify the current volume mode based on the extracted volume features. The volume mode includes low volume mode, medium volume mode, and high volume mode. S4: Dynamically calculate the display parameters of the volume unit table based on the identified volume mode. The display parameters include at least the display range, scale interval, and response time constant. S5: Update the visual display of the volume unit table based on the calculated display parameters.

[0029] First, the audio signal acquisition and preprocessing in step S1 is performed. The audio input signal is acquired in real time to obtain a digital audio data stream. The audio sampling rate can be a standard sampling rate such as 44.1kHz, 48kHz, or 96kHz, and the bit depth supports 16bit, 24bit, or 32bit. Then, the acquired audio signal is preprocessed, including removing DC components, applying a high-pass filter to remove low-frequency noise (cutoff frequency set to 20Hz or 30Hz), and applying an anti-aliasing filter to prevent frequency aliasing. The audio data is processed in frames, with each frame length set to 10-50 milliseconds (e.g., 20 milliseconds). A 50% overlap can be set between frames to ensure smooth transitions.

[0030] In this embodiment, for an audio stream with a sampling rate of 48kHz and a bit depth of 24bit, the system frames the data in 20-millisecond intervals (i.e., 960 sampling points), with a 50% overlap between frames (480 sampling points). A 30Hz high-pass filter is applied to remove low-frequency noise, ensuring the accuracy of subsequent analysis. The preprocessed audio frame data is stored in a circular buffer for use in subsequent steps.

[0031] Next, step S2 involves volume feature extraction and analysis, including: S:01: For the preprocessed audio data, the instantaneous volume value is obtained by calculating the root mean square (RMS) value of each frame of audio signal and converting it to decibels, as follows: , in, Indicates the first The amplitude of each sampling point The number of sampling points in the current frame. The value, when converted to dB units, is represented as: , The peak volume is the maximum volume within the sliding window. The value, average volume, is within the sliding window. The arithmetic mean of the values.

[0032] S202: Within a preset duration sliding window, statistically analyze the distribution characteristics of volume values, including average volume, peak volume, minimum volume, and dynamic range. Specifically, maintain a sliding window (duration 3-10 seconds) and statistically analyze the distribution characteristics of volume values ​​within the window, including: average volume, peak volume, minimum volume, volume standard deviation, and dynamic range (the difference between peak and average values).

[0033] S203: Simultaneously analyze the volume change rate (the difference between the current frame and the previous frame) and the volume change frequency (the number of times the volume changes significantly per unit time) to capture the dynamic behavior trend of the audio signal.

[0034] Furthermore, the extraction of the volume features further includes: S204: Perform Fast Fourier Transform (FFT) on the preprocessed audio data to analyze its frequency distribution and extract spectral features including the proportion of low-frequency energy, mid-frequency energy, and high-frequency energy. S205: Calculate the spectral centroid and spectral bandwidth parameters to help identify the type and characteristics of audio content.

[0035] In this embodiment, for example, within a classical music excerpt, the system maintains a 5-second sliding window. Analysis reveals an average volume of -25dB, a peak volume of -5dB, a dynamic range of 20dB, and a high frequency of volume changes (3-5 significant changes per second). Spectral analysis shows a large proportion of mid-to-low frequency energy, with the spectral centroid located around 800Hz. These characteristics indicate that the audio content possesses a large dynamic range and rich frequency levels.

[0036] Next, step S3, volume pattern recognition and classification, includes: S301: Based on the extracted average volume value, the volume mode is divided into low volume mode, medium volume mode, and high volume mode according to a preset threshold. Specifically, based on the volume features extracted in step S2, we use multi-dimensional judgment rules to identify the current volume mode and define three basic modes: low volume mode (average volume < -30dB), medium volume mode (-30dB ≤ average volume ≤ -10dB), and high volume mode (average volume > -10dB).

[0037] S302: Further subdivide each volume mode into low dynamic mode and high dynamic mode based on the calculated dynamic range. Low dynamic mode: dynamic range <10dB, low volume change rate; high dynamic mode: dynamic range ≥10dB, high volume change rate.

[0038] S303: Employs a state machine model to manage switching between different modes, ensuring stability through setting mode switching thresholds and delay times. Specifically, it sets mode switching thresholds and delay times (e.g., 1-2 seconds), and switching is only performed after the new mode has been consistently stable for more than the delay time.

[0039] S304: Introduces audio content type recognition based on machine learning models to classify audio content into categories such as speech, music, or ambient sound. Different display strategies are applied to different content types.

[0040] S305: Combining audio content type and volume distribution statistical characteristics, the initially identified volume pattern is corrected and optimized to generate a display strategy that better matches the audio characteristics. Specifically, we comprehensively consider the statistical characteristics of volume distribution, such as the skewness and kurtosis of volume values, to determine whether the volume distribution is concentrated or dispersed. High skewness indicates an asymmetrical volume distribution, while high kurtosis indicates a concentrated volume distribution.

[0041] In this embodiment, the system detects a podcast recording with an average volume of -28dB, a dynamic range of 8dB, and a low volume change rate. Spectrum analysis shows concentrated mid-frequency energy (clear vocal characteristics). The overall assessment identifies it as a "low volume - low dynamic range - voice-related" mode. The system sets a 1.5-second delay to confirm the mode's stability before switching modes. Alternatively, if the recording is an electronic dance track with an average volume of -8dB, a dynamic range of 12dB, and frequent peaks reaching -3dB, showing frequent and drastic volume changes, spectrum analysis shows strong low-frequency energy and noticeable high-frequency components. The system identifies it as a "high volume - high dynamic range - music-related" mode.

[0042] In step S4, we dynamically calculate and optimize the display parameters, including: S401: Adaptively adjusts the display range of the volume unit table based on the identified volume mode. In low volume mode, the display range is reduced to enhance the display accuracy of the low volume area, and in high volume mode, the display range is expanded to prevent the display element from touching the top. Specifically, in low volume mode, the display range is set to -60dB to -20dB to increase the display accuracy of the low volume area; in medium volume mode, it is set to -40dB to 0dB; and in high volume mode, it is set to -20dB to +6dB to expand the high volume area to prevent the pointer from touching the top.

[0043] S402: Simultaneously adjusts the density of the scale intervals according to the current mode, and configures different response time constants, including attack time and release time, for low-dynamic and high-dynamic modes respectively. Specifically, in low-volume mode, a fine scale of 2dB or 3dB is used; in high-volume mode, a coarse scale of 5dB or 6dB is used. The scale numbers are automatically adjusted to maintain readability; the VU Meter's attack time and release time are adjusted according to the dynamic mode. High-dynamic mode uses a shorter attack time (10-30ms) and a moderate release time (100-300ms) for rapid response to changes; low-dynamic mode uses longer attack and release times for smooth display.

[0044] S403: Defines a color mapping scheme associated with volume levels, assigning different colors to low, medium, high, and over-limit areas, and dynamically adjusting the transition points between colors. Specifically, it defines a gradient color map, using green for low volume areas, yellow for medium volume areas, orange for high volume areas, and red for areas exceeding the threshold. The color transition points are dynamically adjusted based on the volume mode to ensure visual consistency. The color mapping interpolation formula (linear interpolation) is expressed as follows: , in, Indicates the volume value The color of the place, and Indicates the start and end colors, and This indicates the volume range corresponding to the start and end points.

[0045] S404: Simultaneously implements peak hold functionality, displaying instantaneous peak volume in a specific visual format and adjusting the hold time of the peak indicator according to the current dynamic mode. Specifically, it records and displays the peak volume, with the peak indicator displayed in different colors or shapes, and sets the decay rate (e.g., 20dB per second). In high dynamic mode, the peak hold time is shorter to provide real-time feedback; in low dynamic mode, the peak hold time is longer for observation.

[0046] In this embodiment, for the "Low Volume - Low Dynamic Range - Voice" mode, the system sets the display range to -55dB to -25dB, using 3dB increments, with an attack time of 50ms and a release time of 300ms. In the color mapping, -55dB to -45dB is dark green, -45dB to -35dB is light green, and -35dB to -25dB is yellow-green. The peak hold time is 2 seconds. This setting allows the user to clearly see the volume changes around -50dB during low-volume recording, facilitating precise adjustment of the recording gain. Meanwhile, for the "High Volume - High Dynamic Range - Music" mode, the system expands the display range to -25dB to +6dB, using 5dB increments, with an attack time of 10ms and a release time of 150ms. In the color mapping, -25dB to -10dB is green, -10dB to 0dB is yellow, 0dB to +3dB is orange, and above +3dB is red (warning zone). Peak hold time is 0.5 seconds. This setting allows for rapid response to beat changes in electronic dance music while maintaining ample display space at high volumes.

[0047] Finally, step S5 involves visual rendering and updating the volume unit table (VU Meter), where updating the volume unit table includes: S501: Update the visual elements of the volume unit table based on dynamically calculated display parameters. These visual elements may take one or more forms, including pointer type, LED level bar, digital waveform display, and 3D stereoscopic display.

[0048] S502: When updating the display, a smooth interpolation algorithm is applied to make the movement of the pointer or level bar appear smooth. Specifically, if a pointer-type VU Meter visual element is used, we calculate the pointer angle based on the current volume value and apply a smooth interpolation algorithm (such as a cubic Bézier curve) to make the pointer movement smooth. The damping coefficient of the pointer is dynamically adjusted according to the response time constant. The scale lines and numerical labels of the dial are redrawn according to the display range; In addition, if LED level bars are used as visual elements, the corresponding number of LED units are lit according to the volume value. The color of each LED is set according to a color mapping scheme. A gradient effect is achieved, with the LED corresponding to the current volume using full brightness and adjacent LEDs using partial brightness to form a transition. A peak indicator is added to display the peak position by flashing or highlighting. If using a digital waveform display, plot a real-time graph of the audio waveform, with the horizontal axis representing time and the vertical axis representing amplitude (scaled according to the display area). Fill the waveform area with color, varying the color according to the volume level. Add baselines (such as 0dB lines, -6dB lines) to help users determine the volume level.

[0049] S503: When a volume mode switch is detected, a smooth visual transition is performed on the scale range, color mapping, and display position within a preset transition time. Specifically, a smooth transition animation is added during mode switching, with the scale range and color mapping gradually changing to the new state within 0.5-1 seconds to avoid abrupt jumps. The movement of the pointer or level bar uses an easing function to enhance visual smoothness. In addition, the display refresh rate is dynamically adjusted according to the frequency of volume changes. In high dynamic mode, a higher refresh rate (60fps or higher) is used to capture rapid changes; in low dynamic mode, the refresh rate is reduced (30fps) to save computing resources.

[0050] In this embodiment, we use a pointer-type VU Meter where the system detects a switch from "medium volume" mode to "low volume" mode. During the 0.8-second transition period, the dial range smoothly changes from -40dB to 0dB to -60dB to -20dB, with the scale lines and numerical markings updated synchronously. The pointer movement uses cubic Bézier curve interpolation, smoothly moving from its original position pointing to -30dB to the corresponding position under the new scale system. The color mapping also changes synchronously, making the entire transition smooth and natural. The cubic Bézier curve interpolation formula is as follows: , in, Indicates the pointer position after interpolation. , , and Indicates the coordinates of the control points. Indicates the normalized time parameter. .

[0051] For example, in the LED level bar display, the system uses a 60fps refresh rate for the "High Volume - High Dynamic Range" mode. The level bar consists of 40 LED units, with 35 LEDs lit according to the current volume value (e.g., -5dB). Of the lit LEDs, the first 25 are green, the next 7 are yellow, and the last 3 are orange. The 35th LED uses full brightness, while the 34th and 36th LEDs use 70% brightness to create a gradient. The peak indicator is displayed at the 38th LED position (peak value -2dB), flashing red to alert the user.

[0052] In addition to the methods mentioned above, it also includes interaction and personalization: S601: The user interface receives manual adjustment commands from the user regarding the display parameters of the volume unit meter. Specifically, we provide a user interface in the system that allows users to manually adjust the display parameters of the VU Meter. Users can choose their preferred display style (pointer, LED, wave), color theme (classic, modern, high contrast), and sensitivity level (high, medium, low).

[0053] S602: Records user adjustment behaviors and preference settings and generates user profiles. Specifically, the system learns user adjustment habits in different scenarios, such as preferring to use low sensitivity and fine scale when recording vocals, and preferring to use high sensitivity and coarse scale when listening to music.

[0054] S603: Based on the user profile and real-time analysis of audio content characteristics, the system learns user habits through machine learning algorithms to optimize the display of the volume unit table. Simultaneously, the system automatically recommends the most suitable combination of display parameters based on the user profile and the current volume mode. Users can apply the recommended settings with a single click or further fine-tune them.

[0055] The personalized volume unit table display optimization further includes: S604: Provides scene preset functionality, allowing users to create and save custom volume unit table configurations for different audio work scenarios. Specifically, users can create and save custom VU Meter configurations for different work scenarios (such as podcast recording, music mixing, and live performance monitoring). The system can automatically detect the current scene and load the corresponding preset.

[0056] S605: The system automatically detects the current work scenario and loads the corresponding preset configuration.

[0057] S606: Simultaneously collects user feedback data on the adaptive display effect and continuously optimizes the volume pattern recognition and display parameter calculation strategies using online learning algorithms. Specifically, it provides a feedback mechanism where users can evaluate the system's adaptive effect (satisfied or dissatisfied). The system collects feedback data and continuously optimizes the volume pattern recognition and display parameter calculation strategies through online learning algorithms (such as reinforcement learning).

[0058] In this embodiment, User A is a podcast host who frequently records talk shows. The system learns that User A repeatedly adjusts the display range to -50dB to -30dB during recording and selects a green theme and low sensitivity settings. The system creates a configuration file for User A, automatically recommending this set of parameters when a "low volume - voice-related" mode is detected. After User A confirms, the system saves it as a "podcast recording" preset. The next time a user records, the system automatically recognizes the scene and loads the preset, eliminating the need for manual adjustment. Alternatively, User B is a music producer with different needs when mixing different styles of music. The system creates three scene presets for User B: "Classical Music Mixing" (high dynamic range, wide display range), "Electronic Music Mixing" (high sensitivity, fast response), and "Ambient Sound Effects Processing" (low sensitivity, smooth display). User B can quickly switch presets via a drop-down menu, and the system completes parameter adjustments and visual updates within one second. User B rates the system's adaptive effect as "satisfactory," and the system records this feedback and increases the weight of the corresponding scene.

[0059] In addition, the method also includes system performance optimization and resource management: S701: Employs a multi-threaded or asynchronous processing architecture to optimize system performance, allocating audio acquisition, feature analysis, pattern recognition, and visual rendering tasks to different execution threads. Specifically, it calculates the complexity of audio feature extraction and volume pattern recognition using FFT. The formula is as follows: , in, This represents the number of sampling points per frame in the FFT. Fast algorithms such as FFT are used to accelerate spectral analysis; simultaneously, the audio acquisition thread is responsible for acquiring audio data in real time, the analysis thread is responsible for feature extraction and pattern recognition, and the rendering thread is responsible for updating the VUMeter's visual representation. Threads communicate with each other through thread-safe queues or message mechanisms.

[0060] S702: Dynamically adjusts the allocation of computing resources based on the real-time system load. Under high load, it reduces the complexity or frequency of feature analysis, while under low load, it improves analysis accuracy and display refresh rate. Specifically, under high system load, it reduces the frequency of feature analysis or simplifies calculations (e.g., reducing the number of FFT points or lowering the update frequency of the sliding window) to ensure smooth operation of core functions. Under low system load, it improves analysis accuracy and refresh rate.

[0061] S703: This invention utilizes a caching mechanism and a lookup table (LUT) to accelerate repetitive computation operations. Specifically, it optimizes memory usage to avoid memory leaks and frequent memory allocations. A circular buffer is used to store audio data and feature values, with a fixed-size memory space pre-allocated and unused data structures periodically cleaned up. Simultaneously, a caching mechanism is implemented to cache frequently used calculation results (such as FFT results and color maps) to avoid redundant calculations. A lookup table (LUT) is used to accelerate time-consuming operations such as logarithmic and trigonometric function calculations. The caching mechanism's lookup table acceleration formula is as follows: , in, This indicates the input value (such as a dB value). This indicates the pre-calculated result (such as color value or converted voltage value) obtained through a lookup table.

[0062] Based on this, the present invention monitors system performance metrics, including CPU utilization, memory utilization, processing latency, and frame rate. When performance issues are detected, optimization strategies are automatically triggered or warnings are issued to the user. Performance diagnostic tools are provided to help developers and advanced users analyze bottlenecks.

[0063] In this embodiment, it is assumed that the system runs on a computer configured with a 4-core CPU and 8GB of memory. The audio acquisition thread runs at the highest priority to ensure real-time performance; the analysis thread runs at medium priority, performing feature extraction and pattern recognition when the CPU is idle; and the rendering thread runs at normal priority to maintain a refresh rate of 60fps. When the system detects that the CPU utilization reaches 80%, it automatically reduces the FFT points from 2048 to 1024 to reduce computation. After optimization, the CPU utilization drops to 60%, and the system still runs smoothly. Furthermore, it is assumed that the system uses a 500MB circular buffer to store 10 seconds of audio data (48kHz sampling rate, 32-bit floating-point, stereo). The buffer uses a circular queue structure, where new data overwrites the oldest data, eliminating the need for frequent memory allocation and deallocation. The color map is pre-calculated and stored in a lookup table, containing 256 color values ​​corresponding to different volume levels. The calculation of dB values ​​uses a lookup table to accelerate the log10 operation, reducing the calculation time from 50 microseconds to 5 microseconds.

[0064] To achieve the VU Meter display optimization of this invention, this invention also introduces multi-scenario application extensions, including: S801: The adaptive volume unit table display method is applied to a multi-channel audio system, calculating the volume characteristics and display parameters for each independent channel, and supporting the simultaneous or composite display of volume unit tables for multiple channels in a single display interface. Specifically, this technology can be integrated into digital audio workstations (DAWs) to provide users with accurate volume monitoring; used in live streaming software to help broadcasters adjust volume in real time; applied in audio players to enhance the listening experience; and supports the independent display of multi-channel audio. For stereo, 5.1 surround sound, 7.1 surround sound, and other multi-channel audio, volume characteristics and display parameters are calculated separately for each channel. Multiple VU Meters can be displayed simultaneously, or a composite view (such as a circular arrangement) can be used to display all channels.

[0065] S802: Enables system compatibility with international loudness standards, supporting switching between traditional volume display and loudness display based on the LUFS (Loudness Units Relative to Full Scale) audio loudness measurement standard. Specifically, it achieves loudness standard compatibility, supporting international loudness standards such as LUFS, EBU R128, and ATSC A / 85. The system can switch between traditional volume display and loudness display, meeting the needs of professional fields such as broadcasting and streaming media.

[0066] S803: The volume unit meter display technology is extended to virtual reality (VR) or augmented reality (AR) environments, enabling immersive audio level visualization rendering in three-dimensional space. Specifically, a VU Meter is rendered in 3D space, providing an immersive audio monitoring experience. It supports directional display of spatial audio, allowing users to intuitively see the volume distribution of sound from different directions.

[0067] This approach provides API interfaces and plugin support, allowing third-party developers to integrate the technology. An SDK (Software Development Kit) is released, providing detailed documentation and sample code. It supports multiple programming languages ​​(such as C++, Python, JavaScript) and platforms (Windows, macOS, Linux, iOS, Android).

[0068] In this embodiment, an adaptive VU Meter is integrated into a professional digital audio workstation (DAW). When recording multi-track vocals, the system independently calculates the volume mode for each track. The lead vocal track is identified as a "medium volume - medium dynamics" mode, with a display range of -35dB to -5dB; the backing vocal tracks are identified as a "low volume - low dynamics" mode, with a display range of -50dB to -20dB. The VU Meter for each track is arranged vertically on the right side of the mixing interface as LED level bars, with optimized colors and scales. Users can easily monitor the volume status of all tracks at a glance.

[0069] In summary, the invention implements a volume unit meter (VU Meter) display method based on adaptive volume mode. This method intelligently identifies the current volume mode by analyzing the volume characteristics and dynamic range of the audio signal in real time, and dynamically adjusts the VU Meter's display parameters, including display range, scale intervals, response time, and color mapping, based on the identification results. The system also supports user-customized learning and scene presets, providing optimal display effects according to user habits and audio content characteristics. Compared to traditional fixed display modes, this invention significantly improves the accuracy and practicality of the VU Meter in various volume scenarios, providing professional audio workers and ordinary users with a more intuitive and accurate audio monitoring tool, contributing to improved audio production quality and user experience.

[0070] Furthermore, this embodiment also provides a volume unit table display optimization system, such as... Figure 2 As shown, it includes: The signal acquisition and preprocessing module 10 is used to acquire audio input signals in real time and preprocess the acquired audio input signals to obtain audio data suitable for analysis. Feature extraction module 20 is used to extract volume features that reflect the intensity and dynamic characteristics of audio signals based on preprocessed audio data; The pattern recognition module 30 is used to identify the current volume mode based on the extracted volume features, wherein the volume mode includes low volume mode, medium volume mode and high volume mode. The dynamic display control module 40 is used to dynamically calculate the display parameters of the volume unit table based on the identified volume mode. The display parameters include at least the display range, scale interval, and response time constant. The visual rendering module 50 is used to update the visual display effect of the volume unit table based on the calculated display parameters.

[0071] It should be noted that the steps in the volume unit table display optimization method provided in this embodiment can be implemented based on the corresponding modules in the volume unit table display optimization system. Those skilled in the art can refer to the technical solution of the system to implement the steps of the method. That is, the embodiments in the system can be understood as preferred examples of implementing the method, and will not be elaborated here.

[0072] This embodiment also provides an electronic device, such as... Figure 3 As shown, the electronic device includes a processor 14 and a memory 13. The memory 13 stores machine-executable instructions that can be executed by the processor 14. The processor 14 executes the machine-executable instructions to implement the above-described volume unit table display optimization method.

[0073] Furthermore, Figure 3 The electronic device shown also includes a bus 12 and a communication interface 11, with the processor 14, the communication interface 11 and the memory 13 connected via the bus 12.

[0074] The memory 13 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 11 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 12 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0075] Processor 14 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 14 or by instructions in software form. Processor 14 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 13. The processor 14 reads the information in memory 13 and, in conjunction with its hardware, completes the steps of the volume unit table display optimization method.

[0076] This disclosure also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, storing a computer program that, when run on a computer, causes the computer to perform the steps of a volume unit table display optimization method.

[0077] Finally, it should be noted that the above description is only a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be pointed out that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for optimizing the display of volume unit tables, characterized in that, Includes the following steps: S1: Real-time acquisition of audio input signals, and preprocessing of the acquired audio input signals to obtain audio data suitable for analysis; S2: Extract volume features that reflect the intensity and dynamic characteristics of audio signals based on preprocessed audio data; S3: Identify the current volume mode based on the extracted volume features. The volume mode includes low volume mode, medium volume mode, and high volume mode. S4: Dynamically calculate the display parameters of the volume unit table based on the identified volume mode. The display parameters include at least the display range, scale interval, and response time constant. S5: Update the visual display of the volume unit table based on the calculated display parameters.

2. The volume unit table display optimization method according to claim 1, characterized in that, In step S2, the extraction of the volume feature includes: For the preprocessed audio data, the instantaneous volume value is obtained by calculating the root mean square (RMS) value of each frame of audio signal and converting it to decibels. Within a preset duration sliding window, the distribution characteristics of volume values ​​are statistically analyzed, including average volume, peak volume, minimum volume, and dynamic range. Simultaneously analyze the rate of change of volume and the frequency of change of volume to capture the dynamic behavior trends of audio signals.

3. The volume unit table display optimization method according to claim 2, characterized in that, The extraction of the volume features further includes: The preprocessed audio data is subjected to Fast Fourier Transform (FFT) to analyze its frequency distribution and extract spectral features including the proportion of low-frequency energy, mid-frequency energy, and high-frequency energy. Calculate the spectral centroid and spectral bandwidth parameters to help identify the type and characteristics of audio content.

4. The volume unit table display optimization method according to claim 2, characterized in that, In step S3, the identification of the volume mode includes: Based on the extracted average volume value, the volume mode is divided into low volume mode, medium volume mode and high volume mode according to a preset threshold; Each volume mode is further subdivided into low dynamic range mode and high dynamic range mode based on the calculated dynamic range; A state machine model is used to manage the switching between different modes, and the stability of mode switching is ensured by setting mode switching thresholds and delay times.

5. The volume unit table display optimization method according to claim 4, characterized in that, The identification of the volume mode further includes: We introduce an audio content type recognition based on a machine learning model to classify audio content into categories such as speech, music, or ambient sound. By combining the audio content type and volume distribution statistics, the initially identified volume patterns are corrected and optimized to generate a display strategy that better matches the audio characteristics.

6. The volume unit table display optimization method according to claim 1, characterized in that, In step S4, the display parameters of the dynamically calculated volume unit table include: The display range of the volume unit table is adaptively adjusted according to the identified volume mode. In low volume mode, the display range is reduced to enhance the display accuracy of the low volume area, and in high volume mode, the display range is expanded to avoid display elements touching the top. Simultaneously, the density of the scale interval is adjusted according to the current mode, and different response time constants, including startup time and release time, are configured in low dynamic mode and high dynamic mode respectively.

7. The volume unit table display optimization method according to claim 1, characterized in that, The display parameters of the dynamically calculated volume unit table further include: Define a color mapping scheme associated with volume levels, assign different colors to low volume, medium volume, high volume and over-limit areas, and dynamically adjust the transition points between colors; It also implements a peak hold function, displaying the instantaneous peak volume in a specific visual form, and adjusting the hold time of the peak indicator according to the current dynamic mode.

8. The volume unit table display optimization method according to claim 1, characterized in that, In step S5, the visual display effect of updating the volume unit table includes: Based on dynamically calculated display parameters, update the visual elements of the volume unit table, which may take one or more forms, including pointer type, LED level bar or digital waveform display; When updating the display, a smooth interpolation algorithm is applied to make the movement of the pointer or level bar present a smooth animation effect; When a volume mode switch is detected, a smooth visual transition is performed on the scale range, color mapping, and display position within a preset transition time.

9. The method for optimizing the display of volume unit tables according to claim 1, characterized in that, The method also includes interaction and personalization: The user interface receives manual adjustment commands from the user for the parameters displayed in the volume unit table; Record user adjustment behaviors and preference settings and generate user profiles; Based on the user profile and real-time analysis of audio content characteristics, machine learning algorithms are used to learn user habits, thereby achieving personalized volume unit table display optimization.

10. The volume unit table display optimization method according to claim 9, characterized in that, The personalized volume unit table display optimization further includes: It provides a scene preset function, allowing users to create and save custom volume unit table configurations for different audio working scenarios; The system automatically detects the current work scenario and loads the corresponding preset configuration; Simultaneously, user feedback data on the adaptive display effect is collected, and online learning algorithms are used to continuously optimize the strategies for volume pattern recognition and display parameter calculation.

11. The method for optimizing the display of volume unit tables according to claim 1, characterized in that, The method also includes system performance optimization and resource management: To optimize system performance, a multi-threaded or asynchronous processing architecture is adopted, and audio acquisition, feature analysis, pattern recognition, and visual rendering tasks are assigned to different execution threads. The allocation of computing resources is dynamically adjusted according to the real-time load of the system, reducing the complexity or frequency of feature analysis under high load and improving analysis accuracy and display refresh rate under low load. Utilize caching mechanisms and lookup tables to accelerate repetitive computational operations.

12. The volume unit table display optimization method according to claim 1, characterized in that, The method also includes multi-scenario application extensions: The adaptive volume unit table display method is applied to a multi-channel audio system, whereby the volume characteristics and display parameters of each independent channel are calculated separately, and the volume unit tables of multiple channels can be displayed simultaneously or synthesized in a single display interface. Make the system compatible with international loudness standards and support switching between traditional volume display and loudness display based on the audio loudness unit LUFS; The volume unit display technology is extended to virtual reality (VR) or augmented reality (AR) environments to achieve immersive audio level visualization rendering in three-dimensional space.

13. A volume unit table display optimization system, characterized in that, include: The signal acquisition and preprocessing module is used to acquire audio input signals in real time and preprocess the acquired audio input signals to obtain audio data suitable for analysis. The feature extraction module is used to extract volume features that reflect the intensity and dynamic characteristics of the audio signal based on the preprocessed audio data. The pattern recognition module is used to identify the current volume mode based on the extracted volume features. The volume modes include low volume mode, medium volume mode and high volume mode. The dynamic display control module is used to dynamically calculate the display parameters of the volume unit table based on the identified volume mode. The display parameters include at least the display range, scale interval, and response time constant. The visual rendering module is used to update the visual display effect of the volume unit table based on the calculated display parameters.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the volume unit table display optimization method as described in any one of claims 1-12.

15. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the volume unit table display optimization method as described in any one of claims 1-12.

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