1415 results about "Vocal sound" patented technology

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health, voice navigation and the like, and discloses a voice processing method, device, equipment and medium based on voiceprint feature screening. The method comprises the following steps: screening voice of a near-field main speaker based on voiceprint similarity and signal intensity, executing duration filtering and confidence verification, dynamically constructing a voiceprint feature library, generating a voiceprint mask matrix based on the voiceprint feature library, performing frequency band suppression on a to-be-processed voice signal, and outputting a purified voice signal. According to the method, the voiceprint library is dynamically constructed, the voiceprint mask matrix is generated based on the voiceprint feature library, frequency band suppression is performed on the to-be-processed voice signal, non-target voiceprints are effectively shielded, the voice signal quality is improved, and thus target voice is accurately captured in a high-noise environment.

The invention relates to the technical field of subtitle translation, in particular to a multi-modal time sequence alignment AI video translation method and system, and the method comprises the steps: 1, carrying out the multi-modal analysis of a to-be-translated video, and obtaining audio separation data, voiceprint feature data and visual time sequence data; 2, performing cross-language translation and context optimization on the basis of the voice of the audio separation data to generate a target language text, and synthesizing target language voice retaining the original voice color in combination with the voiceprint feature data and the target language text; generating a mouth shape animation matched with the target language voice based on the lip key point data and the limb action time sequence data; and step 3, performing four-dimensional alignment on the target language voice, the translated text, the mouth shape animation and the limb action sequence through a cross-modal time sequence encoder, and dynamically adjusting the layout of the bilingual subtitles to adapt to a video picture. According to the method and the device, multi-mode synchronization can be taken into consideration during video translation, so that the body actions such as voice, subtitles and mouth shapes are kept aligned.

The invention belongs to the technical field of three-dimensional reconstruction, and discloses a three-dimensional digital human generation method and system capable of voice interaction. According to the invention, brand new speaking audios in different languages are automatically generated according to different languages of the input target text and the sampled human voice audios; the sequential stability and detail reduction capability of three-dimensional human motion are guaranteed by using multi-model joint estimation and a sequential loss function, and facial expression details and hand postures in the image can be accurately estimated. After the high-precision three-dimensional human body model is obtained through estimation, human body action and expression generation is carried out based on voice driving, accurate synchronization of actions and expressions generated through voice is achieved, and facial expression movement and body posture movement, namely a whole-body three-dimensional human body model, conforming to brand-new speaking audio are accurately generated; and finally, rendering the whole-body three-dimensional human body model into a real digital human capable of voice interaction by using a three-dimensional neural rendering model. According to the invention, the realization of single person picture input, high-precision three-dimensional digital person generation and voice interaction is facilitated.

The invention discloses a KTV intelligent light control method and device. The method comprises the following steps: S1, extracting song feature data from a song requesting system, wherein the song feature data comprises low-frequency energy EL, a rhythm feature Tbeat, an emotion label Cemtion and a refrain time point tcorus; s2, generating a light theme template based on the Cemtion and the Tbeat, and setting a switching time point; s3, separating the microphone signal X (t) into a human voice track V (t) and an accompaniment track A (t) through a pre-trained real-time source separation model; s4, adjusting spotlight parameters according to the volume VdB and pitch change rate # imgabs0 # of the human voice track V (t), and driving an atmosphere lamp according to the frequency spectrum of the accompaniment track A (t); s5, acquiring a personnel distribution thermodynamic diagram H (x, y) through an infrared thermal imaging sensor, and acquiring an action amplitude parameter Mlevel through a millimeter wave radar; and S6, fusing personnel distribution and motion amplitude data. According to the invention, KTV intelligent light control can be carried out more intelligently.

The invention discloses a 3D digital human real-time dialogue interaction system and method, and the method comprises the steps: obtaining a voice signal inputted by a user, carrying out the recognition processing of the voice signal through a voice recognition model, obtaining text data, inputting the text data into a text generation model, and outputting the voice text data, and inputting the text data into a voice synthesis model, and outputting the voice synthesis data. The method comprises the steps of inputting speech synthesis data into a 3D expression generation model, outputting 3D expression data, constructing a sound clone model according to a speech signal and the 3D expression data, outputting human voice audio data, performing fine tuning on the sound clone model, and storing fine tuning parameter data, so that a user and a digital human are in smooth and natural conversation, timeliness and continuity of communication are ensured, and user experience is improved. The user experience is greatly improved; by accessing the knowledge base, the digital person has massive knowledge reserve, valuable suggestions and guidance are provided for the user, the user can individually set the figure background of the digital person, and the emotion connection between the user and the digital person is enhanced.

The invention discloses a real-time interaction 3D digital holographic cabin method based on deep learning and sound cloning. The method comprises the following steps that S1, user data are collected and preprocessed; s2, extracting feature vectors of facial expressions and limb actions; s3, generating speech synthesis data by using the improved GE2E network and a preset target speech text; s4, generating a synthesized voice audio based on the voice synthesis data; s5, generating a three-dimensional digital human motion sequence according to the facial expression feature vector and the body motion feature vector; s6, performing timestamp alignment on the three-dimensional digital human action sequence and the synthesized voice audio, and constructing a synchronous output stream; and S7, rendering the synchronous output stream, and performing three-dimensional visual output. According to the method, the improved GE2E network, deep learning and sound cloning methods are fused, three-dimensional virtual human voice action synchronous control is achieved, and the method has the advantages of being high in real-time performance, high in immersion and natural in interaction.

The invention relates to an intelligent video editing method fusing human face features and human voice features, which comprises the steps of input preprocessing, multi-modal analysis, fusion scoring and automatic editing, and adopts a multi-modal mode for analysis, so that the recall rate of key segments is improved, and the efficiency of editing is improved. A personalized editing strategy is supported, facial expression changes and voice emotion peak values are aligned through dynamic time warping, a CLIP-like structure is used for training a bimodal encoder, the human face and the voice are mapped to a unified vector space, the association weight of the human face and the voice is automatically learned, and the intelligent degree of editing is improved.

The invention relates to the technical field of artificial intelligence and digital audio signal processing, and particularly discloses a Karaoke all-in-one machine intelligent tuning method based on AI sound effect optimization, and the method comprises the steps: obtaining an original mixed audio signal sung by a user, and carrying out the preprocessing; processing the preprocessed audio by using a multi-source audio separation model based on a deep neural network, and extracting a human voice track and accompaniment components; extracting time-frequency domain features from the human voice track, and performing quantitative evaluation on the voice tension degree of the user in combination with the trained voice state recognition model; dynamically adjusting an equalizer and a dynamic range compression parameter according to an evaluation result to realize adaptive sound effect optimization; synthesizing the optimized voice and accompaniment and outputting the synthesized voice and accompaniment to monitoring equipment; and finally, establishing a personalized user behavior feature vector by collecting user singing performance and historical tuning data, and performing online fine tuning on a system strategy based on a deep Q network reinforcement learning mechanism to continuously optimize a tuning effect.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention provides a voice recognition method and device, electronic equipment and a medium, and the method comprises the steps: obtaining a to-be-recognized audio in response to a voice recognition request of a user, sampling the to-be-recognized audio to obtain initial audio data, determining the spectrum feature data of the initial audio data, and storing the spectrum feature data of the initial audio data; processing the initial audio data according to the frequency spectrum feature data to obtain human voice audio data, splitting the human voice audio data to obtain target audio data, inputting the target audio data into a pre-trained voice recognition model for parallel recognition to obtain an initial text sequence output by the voice recognition model, and determining starting and ending time corresponding to words in the initial text sequence according to the human voice audio data, sorting the words in the initial text sequence according to the starting and ending time to obtain a target text sequence, taking the target text sequence as a voice recognition result, and displaying the voice recognition result to a user. The method can improve the recognition accuracy while guaranteeing the recognition speed, and improves the user experience.

Disclosed in the present application is a method for training a speech enhancement network, the method comprising: acquiring a training set, wherein the training set comprises a plurality of training samples, and each training sample comprises sample reference speech, sample comparison speech and mixed speech; performing voiceprint extraction on the sample reference speech to obtain a sample voiceprint vector; performing audio feature extraction on the mixed speech to obtain a sample audio feature; performing, by means of a speech enhancement network, enhancement processing on the basis of the sample voiceprint vector and the sample audio feature, and outputting a predicted audio feature for a sample sound-producing object; on the basis of the predicted audio feature and a comparative audio feature corresponding to the sample comparison speech, determining a training loss value of the speech enhancement network; and iteratively updating weight parameters of the speech enhancement network on the basis of the training loss value until a training ending condition is met. The present application is applied to artificial intelligence, and by means of the present application, a speech enhancement network for suppressing interfering human voices can be trained on the basis of sample reference speech and sample comparison speech which originate from the same sound-producing object, thereby effectively improving the quality of speech enhancement.

The invention discloses a single-channel music separation method based on a U-shaped network, which is used for separating human voice from accompaniment. According to the method, a U-shaped network model comprising an encoder, a connection layer, a bottleneck layer and a decoder is constructed, feature extraction is performed on input through a plurality of continuous encoding units, and then the input enters the bottleneck layer formed by a multi-scale feature extraction module, a dual-path module and a multi-scale feature extraction module. And a connection layer formed by the time-frequency enhancement module and the fusion module is used for connecting the coding unit and the bottleneck layer or the coding unit and the decoding unit, and inputting into the next decoding unit. And finally, the decoder outputs the predicted human voice mask and accompaniment mask. A time-frequency enhancement module is introduced into a jump connection layer, and input music signal features on each frequency box are weighted in a time dimension, so that a network model pays attention to more important time points, and the time sequence modeling capability is improved. And meanwhile, a multi-scale feature extraction module is introduced into a bottleneck layer, so that the music signal feature extraction capability of the model is enhanced.

The invention discloses a live broadcast method and device, equipment and a storage medium, and belongs to the field of human-computer interaction. The method comprises the following steps: receiving an uploading operation of a material video, wherein the material video is obtained by pre-recording; receiving a configuration operation of the live broadcast commodity, wherein the configuration operation is used for configuring commodity information of the live broadcast commodity; and playing a live broadcast video of the first live broadcast room, the live broadcast video comprising a video picture of the material video and an explanation audio, the explanation audio being a human voice audio synthesized based on an explanation text, and the explanation text being generated according to the commodity information and / or the material video. The method can improve the digital human live broadcast efficiency.

The invention relates to the technical field of intelligent speech synthesis, and discloses an intelligent robot speech synthesis method based on deep learning, which comprises the following steps: 1, text processing: receiving an input text, and carrying out word segmentation, part-of-speech tagging and phoneme tagging on the text to obtain a text embedding sequence for a subsequent sentiment analysis step; step 2, sentiment analysis: according to the generated text embedding sequence, performing sentiment analysis on the text by using a BERT-based sentiment classification model to obtain text sentiment embedding for multi-modal sentiment fusion; and step 3, receiving visual input, extracting visual emotion embedding through the convolutional neural network, and inputting the visual emotion embedding into multi-modal emotion fusion. Through the deep learning model, voice data can be learned, natural phonemes, intonation and rhythm characteristics in voice can be extracted, a natural and smooth voice synthesis effect is generated, the voice speed, the intonation and the tone quality are close to those of human voice, and the auditory experience of a user is enhanced.

The invention provides an audio separation method and related equipment. The audio separation method and the related equipment are suitable for a scene in which background music is separated from songs. A to-be-processed mixed audio signal is obtained, and the mixed audio signal is a unified audio signal formed by mixing a plurality of audio signals together through a sound mixer or a sound console. Wherein the plurality of audio signals includes a human voice signal and a background sound (e.g., music sound) signal. And inputting the mixed audio signal into a diffusion model, extracting a human voice signal in the mixed audio signal, and subtracting the human voice signal from the mixed audio signal to obtain a background sound signal in the mixed audio signal. According to the scheme, the generation type model, namely the diffusion model, which is more advanced compared with a deep neural network is adopted to learn distribution of the human voice signals, and compared with a traditional method, the human voice can be estimated more accurately, so that the human voice included in the mixed audio signals is extracted more accurately; and thus, high-quality background sound is separated from the mixed audio signal.

The invention discloses an in-vehicle privacy protection method, which comprises the following steps of: when a privacy protection function is in a first open state, identifying human voice in a vehicle; when at least one target human voice is recognized, an ultrasonic unit in the vehicle is controlled to emit dynamic sound waves based on the dynamic audio file, and the dynamic sound waves are random ultrasonic waves with different frequencies and / or different amplitudes changing along with time or a combination of the random ultrasonic waves with different frequencies and / or different amplitudes changing along with time; the target human voice is the human voice including the preset keyword. According to the privacy protection method and device in the vehicle and the storage medium provided by the invention, the conversation content including the preset keyword can be prevented from being recorded, and the privacy and safety of business conversation and private conversation in the vehicle are improved.

The invention discloses a double-earphone sound effect adjusting method and system, a storage medium and a program product, and relates to the field of loudspeakers, and the method comprises the steps: collecting an environment audio signal through a binaural microphone, and executing spectrum analysis to obtain a frequency distribution characteristic of the environment audio signal; based on a preset human voice frequency range, extracting an audio signal conforming to the human voice feature from the frequency distribution feature as a target audio; calculating the time difference and the intensity difference of the target audio between the left ear and the right ear to obtain the human sound source direction and the relative distance in the environment; acquiring head attitude sensor data, and calculating a matching included angle between the head orientation of the user and the human sound source direction based on the head attitude sensor data; when the matching included angle is smaller than a preset threshold value, increasing the audio signal gain of the head facing the direction; and when the conversation voice of the user is detected, the audio signal gain is kept unchanged. By implementing the application, the interaction efficiency when the double earphones are used can be improved.

The invention discloses an audio and video synchronous detection method and device, electronic equipment, a program product and a medium, and relates to the field of artificial intelligence. Human voice audio and human face video in a to-be-detected video file can be segmented into audio clips and video clips according to preset clip duration; and time sequence feature extraction can be performed on each audio clip to obtain the audio feature of each audio clip, and time sequence feature extraction can be performed on each video clip to obtain the video feature of each video clip. Then, the feature similarity between each audio clip and each video clip can be calculated, and when it is determined that the target feature similarity between the target audio clip and the target video clip at the same segmentation position is not larger than the feature similarity between the target audio clip and at least one other video clip, the target audio clip and the at least one other video clip can be segmented. And judging that the audio and video of the to-be-detected video file are asynchronous, thereby improving the accuracy of audio and video synchronous detection by means of time sequence feature extraction, feature similarity calculation and feature similarity comparison.

The invention provides an earphone, head-mounted equipment and an earphone signal processing method, and belongs to the technical field of earphones. The present disclosure provides an earphone having a first bone conduction microphone (2) and a second bone conduction microphone (3). In the wearing state, the first bone conduction microphone (2) is attached to the head, and therefore first vibration signals collected by the first bone conduction microphone (2) mainly comprise human voice signals and environmental noise signals. The second bone conduction microphone (3) is connected with the clamp (1) of the earphone through the first flexible connection structure (4), and the second bone conduction microphone (3) is separated from the head in the wearing state, so that second vibration signals collected by the second bone conduction microphone (3) mainly comprise environmental noise signals. Thus, the controller in the earphone can perform noise reduction processing on the first vibration signal based on the second vibration signal, the environmental noise signal in the first vibration signal can be effectively reduced, and the call quality of a user is improved.

An AI-powered music registry, collaboration, and workflow management platform that addresses the challenges faced by music industry participants in the digital age. The system comprises a segmentation and hashing subsystem for musical pieces, segments, and isolated elements, enabling the evaluation of uniqueness and the consideration of individual creators' contributions. An artificial intelligence (AI) and machine learning (ML) subsystem is employed for extracting and isolating individual instruments, vocals, and performer contributions, while a component-level tracking module enables enhanced crediting and royalty distribution.

The invention relates to the technical field of intelligent household appliances and man-machine interaction, and discloses an electric cooker anti-noise voice interaction system based on multi-mode perception and a control method, and the system comprises a multi-mode perception and collection unit which synchronously collects millimeter wave radar echo signals and acoustic signals; the signal preprocessing and feature extraction unit is used for extracting a user physiological vibration signal and a three-dimensional space position vector from the radar signal and extracting an acoustic energy envelope and a sound source direction vector from the acoustic signal; the time-space consistency verification unit and the voice gating and recognition unit are used for carrying out time synchronization verification by calculating the correlation between the physiological vibration signal and the acoustic energy envelope, and distinguishing real human voice from an environment false trigger source; meanwhile, space consistency verification is carried out by comparing the user direction of radar positioning with the sound source direction of acoustic positioning, so that non-target human voice interference is eliminated. According to the invention, the anti-interference capability and reliability of voice interaction in a real home environment are improved through double verification on a physical level.

The invention discloses a voice data recognition method and system based on an AI voice algorithm, relates to the technical field of AI voice recognition, and solves the problem that the voice data recognition capability is low. The method comprises the following steps: S1, multi-mode cooperative triggering collection: synchronously collecting lip electromyographic signals and voiceprint features through a multi-mode sensor, an activation instruction is generated through feature fusion, and voice acquisition starting is triggered; s2, AI adaptive noise reduction processing: carrying out noise separation on the original audio signal by adopting a generative adversarial network, separating environmental noise features to generate a dynamic noise reduction mask, and keeping the integrity of human voice features; s3, beam dynamic optimization adjustment: analyzing real-time audio quality based on a reinforcement learning algorithm, dynamically adjusting beam pointing and gain parameters of a microphone array, and focusing a target sound source; and S4, semantic association cache enhancement: carrying out real-time semantic analysis on the collected voice data. According to the invention, the voice data recognition capability of an AI voice algorithm is greatly improved.

The invention discloses an intelligent volume adjusting method and system based on environment sound, and the method is applied to a wireless earphone, and comprises the steps: determining a target prompt word according to an initial prompt word registration form submitted by a user; environment sound sampling data collected by wireless earphones are divided into environment noise data and human voice data, and the wireless earphones comprise a left earphone worn on a left ear and a right earphone worn on a right ear; dividing the human voice data into a plurality of groups of character voice data in one-to-one correspondence with the plurality of groups of characters according to voice features in the human voice data; determining target character sound data corresponding to the phrase whose similarity with the target prompt word is greater than a preset threshold value when identifying that the phrase whose similarity with the target prompt word is greater than the preset threshold value exists in the plurality of groups of character sound data; determining the orientation of human voice data corresponding to the target human voice data; and reducing the volume of the left earphone or the right earphone according to the orientation. According to the invention, the use convenience of the wireless earphone is improved.

The invention discloses an audio recognition and noise reduction processing method and system for a power grid regulation and control workbench environment, and the method comprises the steps: obtaining pure audio signals of all power grid regulation and control workers in different working scenes and different background noises in a dispatching desk, carrying out the cross-domain fusion of two features of a time domain and a frequency domain, and taking the two features as a training set to train a speaker model; voice signals are collected in real time, and pure noise and silence in the voice signals are removed; according to the noise level estimation and the noise power, carrying out weighting processing on the voice signal after the pure noise and the mute are removed; recognizing and separating human voice fragments by adopting a continuous down-sampling and resampling algorithm with multi-resolution characteristics; judging whether the human voice fragment contains a target speaker or not through a speaker model; and generating a plurality of voice segments based on the human voice segments, and extracting target speaking human voice through clustering and a speaker model. The method adapts to environmental changes of regulation and control dispatching desks at all levels, environmental noise can be remarkably reduced, and accurate recognition and extraction of human voices are achieved.

The invention provides a video dubbing language conversion method, a video dubbing language conversion system and related equipment. The method comprises the following steps: acquiring audio track data from a video to be converted; carrying out human voice extraction on the audio track data and classifying according to roles to obtain a single speaker audio of each role; performing voice-to-text conversion on the single speaker audio of each role to obtain an original language copywriting of each role; performing sound cloning on the single speaker audio of each role to obtain a timbre model of each role; performing target language translation on the original language copywriting of each role to obtain a translated copywriting of each role; based on the translation copywriting of each role and the tone model of each role, performing text-to-voice conversion to obtain a translation audio of each role; and performing replacement of each role translation audio on the audio track data in the to-be-converted video to obtain a dubbing conversion video. According to the technical scheme, language video dubbing conversion combined with the tone of the speaker is achieved, the video is more diversified, and the user requirements can be better met.

The invention relates to the technical field of digital persons, in particular to a high-definition digital person video generation method for customizing a real person image, which comprises the following steps of: S1, acquiring audio and video data of a target person of which the real person image needs to be customized; s2, preprocessing the audio and video data; s3, customizing an exclusive voice model according to the audio data; s2, segmenting the audio data in the step S2, using a speech recognition model to recognize segmented audio as a label, and training to obtain a customized exclusive speech model; s4, carrying out real-person image fitting generation on the preprocessed audio and video data; s5, performing combined rendering and output of the audio and the real person dynamic video; and inputting a text, generating an audio according to the exclusive voice model and the input text, rendering a real person dynamic video at the same time, fusing the audio and the real person dynamic video through post-processing, and outputting the fused audio and the real person dynamic video to obtain a high-definition digital human video matched with the voice. According to the invention, low-cost customized digital human video generation is realized, and real human voice cloning is realized.

The invention provides a bone conduction earphone. The bone conduction earphone comprises an earphone body, a hanger, a first microphone, a second microphone and a controller, the first microphone and the second microphone are bone conduction microphones, the hanger is connected with the earphone body, the first microphone is connected with the hanger, and the first microphone is used for fitting the cheekbone area of a wearer; the second microphone is arranged on the earphone body, and a gap is formed between the second microphone and the wearer; the first microphone and the second microphone are electrically connected with the controller; the first microphone is used for collecting human voice of a wearer, converting the human voice into a first signal and transmitting the first signal to the controller, the second microphone is used for collecting environment sound, converting the environment sound into a second signal and transmitting the second signal to the controller, and the controller is used for shielding the second signal and transmitting the first signal. The bone conduction microphone and the bone conduction earphone provided by the invention have a good noise reduction effect.

The embodiment of the invention discloses an audio signal processing method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a start instruction when the start instruction sent by a user is received, and obtaining at least one piece of personal sound audio information at the current moment; obtaining target human voice audio information at the current moment based on a pre-obtained sound effect guidance text and the human voice audio information at the current moment; determining target audio information at the current moment according to the target human voice audio information at the current moment and predetermined background audio information at the current moment; and generating and playing a target audio based on the target audio information. According to the method and the device, the voice audio information in the text deduction scene is flexibly adjusted in real time to be matched with scene changes, so that subtle changes and emotional fluctuations in the performance process can be flexibly dealt with, the audio content better fits the actual deduction condition, and the overall auditory experience and immersion of a user are improved.