Abstract: Methods for modifying audio data include operations for accessing audio data having a first prosody, receiving a target prosody differing from the first prosody, and computing acoustic features representing samples. Computing respective acoustic features for a sample includes computing a pitch feature as a quantized pitch value of the sample by assigning a pitch value, of the target prosody or the audio data, to at least one of a set of pitch bins having equal widths in cents. Computing the respective acoustic features further includes computing a periodicity feature from the audio data. The respective acoustic features for the sample include the pitch feature, the periodicity feature, and other acoustic features. A neural vocoder is applied to the acoustic features to pitch-shift and time-stretch the audio data from the first prosody toward the target prosody.

Abstract: In certain aspects, a method includes receiving a plurality of audio inputs. The method includes determining masking of each audio input of the plurality of audio inputs. The method includes displaying the partial loudness and the masking of each audio input of the plurality of audio inputs in a time domain. Systems and machine-readable media are also provided.

Abstract: Methods for modifying audio data include operations for accessing audio data having a first prosody, receiving a target prosody differing from the first prosody, and computing acoustic features representing samples. Computing respective acoustic features for a sample includes computing a pitch feature as a quantized pitch value of the sample by assigning a pitch value, of the target prosody or the audio data, to at least one of a set of pitch bins having equal widths in cents. Computing the respective acoustic features further includes computing a periodicity feature from the audio data. The respective acoustic features for the sample include the pitch feature, the periodicity feature, and other acoustic features. A neural vocoder is applied to the acoustic features to pitch-shift and time-stretch the audio data from the first prosody toward the target prosody.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for sound quality prediction and real-time feedback about sound quality, such as room acoustics quality and background noise. Audio data can be sampled from a live sound source and stored in an audio buffer. The audio data in the buffer is analyzed to calculate a stream of values of one or more sound quality measures, such as speech transmission index and signal-to-noise ratio. Speech transmission index can be calculated using a convolution neural network configured to predict speech transmission index from reverberant speech. The stream of values can be used to provide real-time feedback about sound quality of the audio data. For example, a visual indicator on a graphical user interface can be updated based on consistency of the values over time. The real-time feedback about sound quality can help users optimize their recording setup.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for sound quality prediction and real-time feedback about sound quality, such as room acoustics quality and background noise. Audio data can be sampled from a live sound source and stored in an audio buffer. The audio data in the buffer is analyzed to calculate a stream of values of one or more sound quality measures, such as speech transmission index and signal-to-noise ratio. Speech transmission index can be calculated using a convolution neural network configured to predict speech transmission index from reverberant speech. The stream of values can be used to provide real-time feedback about sound quality of the audio data. For example, a visual indicator on a graphical user interface can be updated based on consistency of the values over time. The real-time feedback about sound quality can help users optimize their recording setup.

Abstract: Systems, methods, and apparatus for equalization preference learning are provided. An example method includes receiving an audio input with respect to a target sound. The example method includes extracting one or more features from the audio input to provide one or more examples for rating based on the audio input. The example method includes generating a query based on the audio input and the one or more rated examples. The example method includes providing one or more synthesizer suggestion results identified in a search based on the query. The example method includes evaluating the one or more results with respect to the target sound. When one of the results matches the target sound, the example method includes outputting synthesizer parameters associated with the result. When none of the results matches the target sound, the example method includes refining the query for a second search based on feedback with respect to the one or more results.

Abstract: Systems, methods, and apparatus for equalization preference learning are provided. An example method includes receiving an audio input with respect to a target sound. The example method includes extracting one or more features from the audio input to provide one or more examples for rating based on the audio input. The example method includes generating a query based on the audio input and the one or more rated examples. The example method includes providing one or more synthesizer suggestion results identified in a search based on the query. The example method includes evaluating the one or more results with respect to the target sound. When one of the results matches the target sound, the example method includes outputting synthesizer parameters associated with the result. When none of the results matches the target sound, the example method includes refining the query for a second search based on feedback with respect to the one or more results.

Abstract: Systems, methods, and apparatus are provided for equalization preference learning for digital audio modification. A method for listener calibration of an audio signal includes modifying a reference sound using at least one equalization curve; playing the modified reference sound for a listener; accepting listener feedback regarding the modified reference sound; and generating a weighting function based on listener feedback. A listener audio configuration system includes an output providing a sound for listener review; an interface accepting listener feedback regarding the sound; and a processor programming an audio device based on listener feedback.

Abstract: Systems, methods, and apparatus are provided for equalization preference learning for digital audio modification. A method for listener calibration of an audio signal includes modifying a reference sound using at least one equalization curve; playing the modified reference sound for a listener; accepting listener feedback regarding the modified reference sound; and generating a weighting function based on listener feedback. A listener audio configuration system includes an output providing a sound for listener review; an interface accepting listener feedback regarding the sound; and a processor programming an audio device based on listener feedback.

Abstract: Systems, methods, and apparatus are provided for equalization preference learning for digital audio modification. A method for listener calibration of an audio signal includes modifying a reference sound using at least one equalization curve; playing the modified reference sound for a listener; accepting listener feedback regarding the modified reference sound; and generating a weighting function based on listener feedback. A listener audio configuration system includes an output providing a sound for listener review; an interface accepting listener feedback regarding the sound; and a processor programming an audio device based on listener feedback.