A method and system for analyzing audio (eg. music) tracks. A predictive model of the neuro-physiological functioning and response to sounds by one or more of the human lower cortical, limbic and subcortical regions in the brain is described. Sounds are analyzed so that appropriate sounds can be selected and played to a listener in order to stimulate and/or manipulate neuro-physiological arousal in that listener. The method and system are particularly applicable to applications harnessing a biofeedback resource.

A method performed by one or more processors in an information processing device for an electronic musical instrument includes, via the one or more processors: receiving performance data generated by a user performance of the electronic musical instrument; extracting time-series characteristics of a sequence of notes from the performance data; detecting a performance technique from the extracted characteristics; and generating an image data reflecting the detected performance technique and outputting the generated image data.

Despite many practical limitations imposed by mobile device platforms and application execution environments, vocal musical performances may be captured and continuously pitch-corrected for mixing and rendering with backing tracks in ways that create compelling user experiences. Based on the techniques described herein, even mere amateurs are encouraged to share with friends and family or to collaborate and contribute vocal performances as part of virtual “glee clubs.” In some implementations, these interactions are facilitated through social network- and/or eMail-mediated sharing of performances and invitations to join in a group performance. Using uploaded vocals captured at clients such as a mobile device, a content server (or service) can mediate such virtual glee clubs by manipulating and mixing the uploaded vocal performances of multiple contributing vocalists.

Systems and methods augment a target media with a plurality of source media. The target media and source media are processed to form time frequency distributions (TFDs). Target features are extracted from the associated TFD and source features are extracted from each of the associated source TFDs. The target features are segmented into temporal portions that are compared with each of the plurality of source features to determine one or more matched source features having nearest matches to the target feature segments. Portions of the source media associated with the matched source features are mixed with the target media to form an augmented target media, wherein the mixing is based upon a probabilistic mixing algorithm that uses a distance between the matched target feature and source features to define an amplitude of each portion of the source media.

In at least one embodiment, a method of performing automatic transcription of musical content included in an audio signal received by a computing device is provided. The method includes processing, using the computing device, the received audio signal to extract musical information characterizing at least a portion of the musical content and generating, using the computing device, a plurality of musical notations representing alternative musical interpretations of the extracted musical information. The method further includes applying a selected one of the plurality of musical notations for transcribing the musical content of the received audio signal.

A method and apparatus to facilitate tone challenged singers to sing intended notes. In one aspect, the singer determines a note to sing corresponding to an intended frequency f.sub.i. The singer utters a note continuously with fundamental frequency f.sub.u into a microphone of the natural ear apparatus. The note is processed by the apparatus to produce sound emphasizing the fundamental frequency f.sub.u and output through a speaker to the auditory organs of the singer. The singer detects differences between intended frequency f.sub.i and uttered fundamental frequency f.sub.u. The singer adjusts his vocal organs as he utters the note with the intention of changing f.sub.u to reduce difference between f.sub.i and f.sub.u.

Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

System, apparatus and method for determining semantic information from audio, where incoming audio is sampled and processed to extract audio features, including temporal, spectral, harmonic and rhythmic features. The extracted audio features are compared to stored audio templates that include ranges and/or values for certain features and are tagged for specific ranges and/or values. Extracted audio features that are most similar to one or more templates from the comparison are identified according to the tagged information. The tags are used to determine the semantic audio data that includes genre, instrumentation, style, acoustical dynamics, and emotive descriptor for the audio signal.

Methods and systems are described that are utilized for remotely controlling a musical instrument. A first digital record comprising musical instrument digital commands from a first electronic instrument for a first item of music is accessed. The first digital record is transmitted over a network using a network interface to a remote, second electronic instrument for playback to a first user. Optionally, video data is streamed to a display device of a user while the first digital record is played back by the second electronic instrument. A key change command is transmitted over the network using the network interface to the second electronic instrument to cause the second electronic instrument to playback the first digital record for the first item of music in accordance with the key change command. The key change command may be transmitted during the streaming of the video data.

A performance analysis method is realized by a computer and includes acquiring a time series of input data representing played pitch that is played, inputting the acquired time series of input data into an estimation model that has learned a relationship between a plurality of items of training input data representing pitch and a plurality of items of training output data representing an acoustic effect to be added to sound having the pitch, and generating a time series of output data for controlling an acoustic effect to be added to sound having the played pitch represented by the acquired time series of input data.