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.

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.

Method of teaching a vocal harmony involves a computing device which automatically generates a plurality of audio presentations of a musical composition in a predetermined series. Each audio presentation in the series is different from the other audio presentations in the series and is configured to assist the user in progressively learning the selected vocal harmony part. Each of the plurality of audio presentations in the predetermined series is made different from others of the audio presentation in the predetermined series by selectively controlling (1) the particular ones of the plurality of vocal harmony parts that are included in each of the audio presentations, and/or (2) a magnitude of an audio volume that is applied to each of the plurality of vocal harmony parts that is included in each of the audio presentations.

Collaboratively creating musical harmonies includes receiving a user selection of a particular harmony. In response to this selection, there is displayed on a display screen of a computing device a plurality of musical note indicators or notes to specify a first harmony part of a musical piece to be performed. Real-time pitch detection is used to determine a pitch of each note which is voiced by a person, and a graphic indication of the actual pitch which is sung is displayed in conjunction with the musical note indicators.

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.

In an aspect, an apparatus for attribute path generation is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user data. At least a processor configured to identify a plurality of attributes of user data. At least a processor is configured to compare an attribute to an improvement threshold. At least a processor is configured to determine an objective as a function of a comparison. At least a processor is configured to create an attribute path including an objective. The attribute path may be displayed to a user by way of a metamap.

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.

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.

A chord judging apparatus for judging chords of a musical piece, is provided with a processor and a memory for storing data of the musical piece, wherein the processor specifies plural segments in the data of the musical piece, estimates a tonality of each of the specified segments based on component tones included in the segment, and judges a chord of the plural segments of the musical piece based on modulation in tonality, when modulation is introduced in the estimated tonalities of the plural segments.

Method of teaching a vocal harmony involves a computing device which automatically generates a plurality of audio presentations of a musical composition in a predetermined series. Each audio presentation in the series is different from the other audio presentations in the series and is configured to assist the user in progressively learning the selected vocal harmony part. Each of the plurality of audio presentations in the predetermined series is made different from others of the audio presentation in the predetermined series by selectively controlling (1) the particular ones of the plurality of vocal harmony parts that are included in each of the audio presentations, and/or (2) a magnitude of an audio volume that is applied to each of the plurality of vocal harmony parts that is included in each of the audio presentations.