System and method for recognising chords in music

A data processing system comprising:

• at least one processor;
• and
• at least one memory storing instructions that, when executed by the at least one processor, cause the data processing system at least to:
• receive music data for a time interval;
• process the music data in a machine learning model to output chord data corresponding to the time interval, the chord data comprising a set of chord features;
• wherein the processing in the machine learning model comprises:
• predicting a value of a chord feature from a set of chord features using an Autoregressive Distribution Estimator (ADE);
• modifying the ADE using the predicted value for the chord feature;
• use the modified ADE to predict a value for a different feature of the chord from the set of chord features;
• repeat the modifying and predicting until a value for each of the features in the set of chord features have been predicted.

A non-transitory computer-readable medium comprising instructions which, when executed by a processor in a computing system, cause the computer to:

• receive music data for a time interval;
• process the music data in a machine learning model to output chord data corresponding to the time interval, the chord data comprising a set of chord features;
• wherein the processing in the machine learning model comprises:
• predicting a value of a chord feature from a set of chord features using an Autoregressive Distribution Estimator (ADE);
• modifying the ADE using the predicted value for the chord feature;
• use the modified ADE to predict a value for a different feature of the chord from the set of chord features;
• repeat the modifying and predicting until a value for each of the features in the set of chord features have been predicted.