Abstract: A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals.

Abstract: Scalable similarity-based generation of compatible music mixes. Music clips are projected in a pitch interval space for computing musical compatibility between the clips as distances or similarities in the pitch interval space. The distance or similarity between clips reflects the degree to which clips are harmonically compatible. The distance or similarity in the pitch interval space between a candidate music clip and a partial mix can be used to determine if the candidate music clip is harmonically compatible with the partial mix. An indexable feature space may be both beats-per-minute (BPM)-agnostic and musical key-agnostic such that harmonic compatibility can be quickly determined among potentially millions of music clips. A graphical user interface-based user application allows users to easily discover combinations of clips from a library that result in a perceptually high-quality mix that is highly consonant and pleasant-sounding and reflects the principles of musical harmony.

Abstract: A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals.

Abstract: A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals.

Abstract: Methods and systems for audio source separation in real-time are described. In an embodiment, the present disclosure describes reading and decoding an audio source into PCM samples, fragmenting Pulse Code Modulation (PCM) samples into fragments, transforming fragments into spectrograms, performing audio source separation using a training database that includes a training dictionary and non-negative matrix factorization (NMF) to generate a set of component signals, and streaming the component signals to a playback engine. In an embodiment, a semantic equalizer graphical user allows for fading of individual component signals.

Abstract: Techniques for generating a virtual music experience. The techniques include source separating an arbitrary digital audio input into a plurality of source-separated tracks. Sets of music features are determined from the plurality of source-separated tracks and provided to a video presentation system at a video frame rate of the video presentation system. The providing the sets of music features to the video presentation system causes the video presentation system to animate one or more graphical assets based on the provided sets of music features.

Abstract: Methods and systems for audio source separation in real-time are described. In an embodiment, the present disclosure describes reading and decoding an audio source into PCM samples, fragmenting Pulse Code Modulation (PCM) samples into fragments, transforming fragments into spectrograms, performing audio source separation using a deep neural network (DNN) to generate an estimated magnitude spectrogram of the component(s) of the audio source, reconstructing the estimated time domain component signals, and streaming the component signals to a playback engine. In an embodiment, a semantic equalizer graphical user allows for real-time mixing of individual component signals.

Abstract: Methods and systems for audio source separation in real-time are described. In an embodiment, the present disclosure describes reading and decoding an audio source into PCM samples, fragmenting Pulse Code Modulation (PCM) samples into fragments, transforming fragments into spectrograms, performing audio source separation using a deep neural network (DNN) to generate an estimated magnitude spectrogram of the component(s) of the audio source, reconstructing the estimated time domain component signals, and streaming the component signals to a playback engine. In an embodiment, a semantic equalizer graphical user allows for real-time mixing of individual component signals.

Abstract: Methods and systems for audio source separation in real-time are described. In an embodiment, the present disclosure describes reading and decoding an audio source into PCM samples, fragmenting Pulse Code Modulation (PCM) samples into fragments, transforming fragments into spectrograms, performing audio source separation using a training database that includes a training dictionary and non-negative matrix factorization (NMF) to generate a set of component signals, and streaming the component signals to a playback engine. In an embodiment, a semantic equalizer graphical user allows for fading of individual component signals.

Abstract: Techniques for generating a virtual music experience. The techniques include source separating an arbitrary digital audio input into a plurality of source-separated tracks. Sets of music features are determined from the plurality of source-separated tracks and provided to a video presentation system at a video frame rate of the video presentation system. The providing the sets of music features to the video presentation system causes the video presentation system to animate one or more graphical assets based on the provided sets of music features.

Abstract: Methods and systems for audio source separation in real-time are described. In an embodiment, the present disclosure describes reading and decoding an audio source into PCM samples, fragmenting Pulse Code Modulation (PCM) samples into fragments, transforming fragments into spectrograms, performing audio source separation using a training database that includes a training dictionary and non-negative matrix factorization (NMF) to generate a set of component signals, and streaming the component signals to a playback engine. In an embodiment, a semantic equalizer graphical user allows for fading of individual component signals.

Abstract: Methods and systems for audio source separation in real-time are described. In an embodiment, the present disclosure describes reading and decoding an audio source into PCM samples, fragmenting Pulse Code Modulation (PCM) samples into fragments, transforming fragments into spectrograms, performing audio source separation using a training database that includes a training dictionary and non-negative matrix factorization (NMF) to generate a set of component signals, and streaming the component signals to a playback engine. In an embodiment, a semantic equalizer graphical user allows for fading of individual component signals.