Abstract: Embodiments are disclosed for identifying matching content using neural content fingerprints. The method may include receiving a request to identify content matching a query content item, wherein the query content item is a time varying content item, generating, by an embedding network, a neural fingerprint for the query content item, identifying one or more candidate content items based on the neural fingerprint of the query content item, determining, by a ranking network, one or more similarity scores corresponding to the one or more candidate content items, and identifying one or more matching content items based on the one or more similarity scores.

Abstract: Embodiments are disclosed for performing a filler word detection process on input audio by a media editing system using trained neural networks. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence, analyzing the audio sequence to determine filler word candidates, classifying, by a filler word classification model, each filler word candidate of the filler word candidates into one of a set of categories, and generating an output audio sequence, the output audio sequence including an identification of a subset of the filler word candidates in a filler words category of the set of categories as identified filler words.

Abstract: Embodiments are disclosed for determining an answer to a query associated with a graphical representation of data. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an unprocessed audio sequence and a request to perform an audio signal processing effect on the unprocessed audio sequence. The one or more embodiments further include analyzing, by a deep encoder, the unprocessed audio sequence to determine parameters for processing the unprocessed audio sequence. The one or more embodiments further include sending the unprocessed audio sequence and the parameters to one or more audio signal processing effects plugins to perform the requested audio signal processing effect using the parameters and outputting a processed audio sequence after processing of the unprocessed audio sequence using the parameters of the one or more audio signal processing effects plugins.

Abstract: Embodiments are disclosed for performing a using a neural network to optimize filter weights of an adaptive filter. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving, by a filter, an input audio signal, wherein the input audio signal is a far-end audio signal, the filter including a transfer function with adaptable filter weights, generating a response audio signal modeling the input audio signal passing through the acoustic environment, receiving a target response signal, including the input audio signal and near-end audio signals, calculating an adaptive filter loss, generating, by a trained recurrent neural network, a filter weight update using the calculated adaptive filter loss, updating the adaptable filter weights of the transfer function to create an updated transfer function, generating an updated response audio signal based on the updated transfer function, and providing the updated response audio signal as an output audio signal.

Abstract: Embodiments are disclosed for performing a section-based, within-song music similarity search by an audio recommendation system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and a request to determine similar audio sequences to the audio sequence from a pre-processed audio catalog, analyzing the audio sequence to generate an audio embedding for the audio sequence, querying a pre-processed audio catalog to retrieve audio embeddings for catalog audio sequences at different time resolutions, generating a set of candidate audio sequences from the pre-processed audio catalog based on the audio embedding for the audio sequence, and providing the set of candidate audio sequences.

Abstract: In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

Abstract: Embodiments are disclosed for generating an audio segmentation of an audio sequence using deep embeddings. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and extracting features for each frame of the audio sequence, where each frame is associated with a beat of the audio sequence. The method may further comprise clustering frames of the audio sequence into one or more clusters based on the extracted features and generating segments of the audio sequence based on the clustered frames, where each segment includes frames of the audio sequence from a same cluster. The method may further comprise constructing a multi-level audio segmentation of the audio sequence and performing a segment fusioning process that merges shorter segments with neighboring segments based on cluster assignments.

Abstract: In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

Abstract: Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

Abstract: Embodiments are disclosed for determining an answer to a query associated with a graphical representation of data. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an unprocessed audio sequence and a request to perform an audio signal processing effect on the unprocessed audio sequence. The one or more embodiments further include analyzing, by a deep encoder, the unprocessed audio sequence to determine parameters for processing the unprocessed audio sequence. The one or more embodiments further include sending the unprocessed audio sequence and the parameters to one or more audio signal processing effects plugins to perform the requested audio signal processing effect using the parameters and outputting a processed audio sequence after processing of the unprocessed audio sequence using the parameters of the one or more audio signal processing effects plugins.

Abstract: In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

Abstract: In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

Abstract: A device implementing a system for mitigating noise includes at least one processor configured to receive a first audio signal corresponding to a first microphone, and determine whether wind noise is present based at least in part on the first audio signal. The processor is configured to select, based on the determining, a second audio signal from between second and third microphones. The second microphone is disposed at a location that experiences less echo coupling when the device is in a particular orientation with respect to a user. The third microphone is disposed at another location that experiences less wind noise. The processor is configured to determine voice and noise reference values based on the first and the selected second audio signals, and perform noise suppression with respect to at least one of the first or the selected second audio signal, based on the voice or the noise reference value.

Abstract: Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

Abstract: A device implementing a system for mitigating noise includes at least one processor configured to receive a first audio signal corresponding to a first microphone, and determine whether wind noise is present based at least in part on the first audio signal. The processor is configured to select, based on the determining, a second audio signal from between second and third microphones. The second microphone is disposed at a location that experiences less echo coupling when the device is in a particular orientation with respect to a user. The third microphone is disposed at another location that experiences less wind noise. The processor is configured to determine voice and noise reference values based on the first and the selected second audio signals, and perform noise suppression with respect to at least one of the first or the selected second audio signal, based on the voice or the noise reference value.

Abstract: A method for controlling a speech enhancement process in a far-end device, while engaged in a voice or video telephony communication session over a communication link with a near-end device. A near-end user speech signal is produced, using a microphone to pick up speech of a near-end user, and is analyzed by an automatic speech recognizer (ASR) without being triggered by an ASR trigger phrase or button. The recognized words are compared to a library of phrases to select a matching phrase, where each phrase is associated with a message that represents an audio signal processing operation. The message associated with the matching phrase is sent to the far-end device, which is used to configure the far-end device to adjust the speech enhancement process that produces the far-end speech signal. Other embodiments are also described.

Abstract: Signals are received from audio pickup channels that contain signals from multiple sound sources. The audio pickup channels may include one or more microphones and one or more accelerometers. Signals representative of multiple sound sources are generated using a blind source separation algorithm. It is then determined which of those signals is deemed to be a voice signal and which is deemed to be a noise signal. The output noise signal may be scaled to match a level of the output voice signal, and a clean speech signal is generated based on the output voice signal and the scaled noise signal. Other aspects are described.

Abstract: Signals are received from audio pickup channels that contain signals from multiple sound sources. The audio pickup channels may include one or more microphones and one or more accelerometers. Signals representative of multiple sound sources are generated using a blind source separation algorithm. It is then determined which of those signals is deemed to be a voice signal and which is deemed to be a noise signal. The output noise signal may be scaled to match a level of the output voice signal, and a clean speech signal is generated based on the output voice signal and the scaled noise signal. Other aspects are described.

Abstract: A method for controlling a speech enhancement process in a far-end device, while engaged in a voice or video telephony communication session over a communication link with a near-end device. A near-end user speech signal is produced, using a microphone to pick up speech of a near-end user, and is analyzed by an automatic speech recognizer (ASR) without being triggered by an ASR trigger phrase or button. The recognized words are compared to a library of phrases to select a matching phrase, where each phrase is associated with a message that represents an audio signal processing operation. The message associated with the matching phrase is sent to the far-end device, which is used to configure the far-end device to adjust the speech enhancement process that produces the far-end speech signal. Other embodiments are also described.

Abstract: System of noise reduction for mobile devices includes blind source separator (BSS) and noise suppressor. BSS receives signals from at least two audio pickup channels. BSS includes sound source separator, voice source detector, equalizer, and auto-disabler. Sound source separator generates signals representing first sound source and second sound source based on signals from the first and the second channels. Voice source detector determines whether the signals representing the first and second sound sources are voice signal or noise signal, respectively. Equalizer scales noise signal to match a level of the voice signal, and generates scaled noise signal. Auto-disabler determines whether to disable BSS. Auto-disabler outputs signals from the at least two audio pickup channels when the BSS is disabled and outputs the voice signal and the scaled noise signal when the BSS is not disabled. Noise suppressor generates clean signal based on outputs from auto-disabler. Other embodiments are also described.