Crowd-sourced curation and training of ai models for karaoke-style performances

A method, comprising:

• accessing computer readable encodings of respective dry vocal performances each captured in connection with one or more respective temporally-synchronized tracks;
• extracting a set of computationally-derived features and metadata from each of the computer readable encodings of the respective dry vocal performances;
• selecting from the computer readable encodings of the respective dry vocal performances, a subset of the dry vocal performances to define a training dataset, wherein the subset of dry vocal performances is selected for correspondence with at least one of the computationally-derived features and the metadata of the respective dry vocal performances;
• training a neural network model using the training dataset to generate a trained neural network model;
• receiving a newly captured computer readable encoding of a dry vocal performance;
• and
• converting, using the trained neural network model, the newly captured dry vocal performance into a modified vocal performance corresponding to the newly captured dry vocal performance but having correspondence with the at least one of the computationally-derived features and the metadata of the respective dry vocal performances.

A method, comprising:

• capturing, via a microphone of a portable computing device, a vocal audio performance of a user of the portable computing device captured in connection with one or more respective temporally-synchronized tracks;
• transmitting a computer readable encoding of the vocal audio performance and a selection of a style or character of a target performance to a service platform;
• and
• receiving, from the service platform for rendering at the portable computing device, a transformed vocal audio performance corresponding to the captured vocal audio performance of the user but with the style or character of the target performance;
• wherein the transformed vocal audio performance includes an output of a neural network model trained with a training dataset comprising previously recorded dry vocal performances each corresponding with at least one of plural computationally-derived features and metadata of the previously recorded dry vocal performances, and wherein the training dataset embodies the style or character of the target performance.

A system, comprising:

• a geographically distributed set of network-connected devices configured to capture vocal audio performances captured in connection with a temporally-synchronized track;
• and
• a service platform configured to (i) curate a dataset of computer readable encodings of dry vocal performances each captured against one or more respective temporally-synchronized tracks, (ii) based on one or more of correspondence of metadata associated with respective ones of the dry vocal performances, correspondence of a set of computationally-derived features extracted from the computer readable encodings of respective dry vocal performances, and correspondence of amongst one or more of the respective temporally-synchronized tracks against which respective ones of the dry vocal performances were captured, selecting from the dataset a subset of the dry vocal performances as a first training set that embodies a first target performance style or character, and (iii) using a first trained neural network model trained with the first training set, generating from at least speech content representations extracted from a newly captured dry vocal performance captured from one of the geographically distributed set of network-connected devices, a transformed vocal performance corresponding to the newly captured dry vocal performance but with the style or character of the first target performance.

A system, comprising:

• a geographically distributed set of network-connected devices configured to capture vocal audio performances captured in connection with a temporally-synchronized track;
• and
• a service platform configured to (i) access computer readable encodings of respective dry vocal performances each captured in connection with one or more respective temporally-synchronized tracks, (ii) extract a set of computationally-derived features and metadata from each of the computer readable encodings of the respective dry vocal performances, (iii) select from the computer readable encodings of the respective dry vocal performances, a subset of the dry vocal performances to define a training dataset, wherein the subset of dry vocal performances is selected for correspondence with at least one of the computationally-derived features and the metadata of the respective dry vocal performances, (iv) train a neural network model using the training dataset to generate a trained neural network model, (v) receive a newly captured computer readable encoding of a dry vocal performance from at least one of the geographically distributed set of network-connected devices, and (vi) convert, using the trained neural network model, the newly captured dry vocal performance into a modified vocal performance corresponding to the newly captured dry vocal performance but having correspondence with the at least one of the computationally-derived features and the metadata of the respective dry vocal performances.

A method, comprising:

• extracting, from computer readable encodings of dry vocal performances each captured in connection with one or more respective temporally-synchronized tracks, performance features associated with each of the dry vocal performances;
• creating a searchable performance features index for storing the extracted performance features associated with each of the dry vocal performances;
• searching the searchable performance features index to select a subset of dry vocal performances that each correspond to a chosen criteria of the extracted performance features, wherein the selected subset of dry vocal performance defines a training dataset that embodies a target performance style or character associated with the chosen criteria;
• and
• using a trained neural network model trained with the training dataset, generating from at least speech content representations extracted from a newly captured dry vocal performance, a transformed vocal performance corresponding to the newly captured dry vocal performance but with the style or character of the target performance.