A device is provided as part of a system, the device being for capturing vibrations produced by an object such as a musical instrument. Via a fixation element, the device is fixed to a drum. The device has a sensor spaced apart from a surface of the drum, located relative to the drum, and a magnet adjacent the sensor. The fixation element transmits vibrations from its fixation point on the drum to the magnet. Vibrations from the surface of the drum and from the magnet are transmitted to the sensor. A method may further be provided for interpreting an audio input, such as the output of the sensors within the system, the method comprising identifying an audio event or grouping of audio events within audio data, generating a model of the audio event that includes a representation of a timbre characteristic, and comparing that representation to expected representations.

A method for automatically forming a master digital audio track is disclosed. A processing device of a digital audio workstation (DAW) may receive a plurality of audio tracks. For each audio track of a plurality of audio tracks, the processing device may place one or more split points at one or more locations on the audio track to produce a plurality of segments that are free of sudden changes in one or more properties of a waveform corresponding to the track. For each audio track of a plurality of audio tracks, the processing device may score each segment of the plurality of segments according to at least one of how closely a pitch of the corresponding waveform is in tune and a degree to which the waveform in the segment surpasses a pre-determined threshold of volume. The processing device may align the plurality of segments of each track of the plurality of audio tracks according to corresponding split points across the plurality of audio tracks. The processing device may select one or more best scoring segments from the plurality of aligned segments to produce a suggested master digital audio track. The processing device may present the suggested master digital audio track in an editing window of a monitor associated with the DAW.

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.

A method including: receiving a music input; determining values of musical parameters based on the input; generating a spatial representation of the music input based on the values; and at a plurality of haptic actuators defining a spatial distribution, cooperatively producing a haptic output based on the spatial representation. A method including: mechanically coupling haptic actuators defining a multidimensional array to a user; receiving a music input; generating a spatial representation of the music input defined on a multidimensional space, wherein the multidimensional space and the multidimensional array have equal dimensionality; and, for each haptic actuator: based on the haptic actuator location within the multidimensional array, determining a corresponding location within the multidimensional space; based on a value of the spatial representation associated with the corresponding location, determining an actuation intensity; and controlling the haptic actuator to actuate based on the actuation intensity.

Technologies are described for identifying familiar or interesting parts of music content by analyzing changes in vocal power using frequency spectrums. For example, a frequency spectrum can be generated from digitized audio. Using the frequency spectrum, the harmonic content and percussive content can be separated. The vocal content can then be separated from the harmonic and/or percussive content. The vocal content can then be processed to identify surge points in the digitized audio. In some implementations, the vocal content is included in the harmonic content during the separation procedure and is then separated from the harmonic content.

A method for processing a music performance, the method comprising the steps of: receiving a first media signal from a media source; analyzing the first media signal to extract any media signal characteristics; creating a reference media signal by suppressing at-least a predominant sound source of the first media signal; reproducing the reference media signal while receiving a users media signal from an input device to generate a second media signal; analyzing the second media signal to extract any media signal characteristics; processing the characteristics of the second media signal in isolation or in combination with the characteristics of the first media signal; and generating feedback for the music performance based upon the processed media signals.

Captured vocals may be automatically transformed using advanced digital signal processing techniques that provide captivating applications, and even purpose-built devices, in which mere novice user-musicians may generate, audibly render and share musical performances. In some cases, the automated transformations allow spoken vocals to be segmented, arranged, temporally aligned with a target rhythm, meter or accompanying backing tracks and pitch corrected in accord with a score or note sequence. Speech-to-song music applications are one such example. In some cases, spoken vocals may be transformed in accord with musical genres such as rap using automated segmentation and temporal alignment techniques, often without pitch correction. Such applications, which may employ different signal processing and different automated transformations, may nonetheless be understood as speech-to-rap variations on the theme.

A method for automatically starting an audio recording that includes receiving audio data and dividing the audio data into a first set of consecutive segments and a second set of consecutive segments that occur after the first set. The method further includes analyzing the first set of segments by measuring an average energy and peak value for each segment of the first set and determining a silence score therefrom, and analyzing the second set of segments by measuring an average energy and peak value for each segment of the second set and determining an music score therefrom. The method begins a recording of the audio data if the silence score is above a first predetermined value and the music score is above a second predetermined value.

A device is provided as part of a system, the device being for capturing vibrations produced by an object such as a musical instrument. Via a fixation element, the device is fixed to a drum. The device has a sensor spaced apart from a surface of the drum, located relative to the drum, and a magnet adjacent the sensor. The fixation element transmits vibrations from its fixation point on the drum to the magnet. Vibrations from the surface of the drum and from the magnet are transmitted to the sensor. A method may further be provided for interpreting an audio input, such as the output of the sensors within the system, the method comprising identifying an audio event or grouping of audio events within audio data, generating a model of the audio event that includes a representation of a timbre characteristic, and comparing that representation to expected representations.

The present disclosure presents a novel approach to automatic transcription of piano music in a context-dependent setting. Embodiments described herein may employ an efficient algorithm for convolutional sparse coding to approximate a music waveform as a summation of piano note waveforms convolved with associated temporal activations. The piano note waveforms may be pre-recorded for a particular piano that is to be transcribed and may optionally be pre-recorded in the specific environment where the piano performance is to be performed. During transcription, the note waveforms may be fixed and associated temporal activations may be estimated and post-processed to obtain the pitch and onset transcription. Experiments have shown that embodiments of the disclosure significantly outperform state-of-the-art music transcription methods trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.