System and method for automatic music generation using a neural network architecture

A method of automatically generating music on the basis of an initial note sequence input, the method comprising steps of:

• interpreting each note in the initial input sequence, thereby to generate current note pitch data, current note duration data and current note musical context data;
• generating a subsequent note duration output on the basis of the current note duration data and the current note context data using a rhythm production part;
• storing the current musical context data and note duration information in one or more state units associated with the rhythm production part;
• generating a subsequent note using a note generation part on the basis of the subsequent note duration output, the current note pitch data, the current note musical context data, the current note duration data, and duration and pitch information stored in state units associated with the note generation part;
• and
• feeding back the pitch and duration of the subsequent note back to the rhythm generation and note generation parts, the subsequent note thereby becoming the current note for a following iteration.

A computer program product including a computer readable medium having recorded thereon a computer program for automatically generating music on the basis of an initial note sequence input, the computer program comprising:

• interpretation process steps arranged to interpret each note in the initial input sequence, thereby generating current note pitch data, current note duration data, and current note musical context data;
• generating process steps arranged to generate a subsequent note duration output on the basis of the current note duration data and the current note context data using a rhythm production part;
• storing process steps arranged to store the current musical context data and note duration information in one or more state units associated with the rhythm production part;
• generation process steps arranged to generate a subsequent note using a note generation part on the basis of the subsequent note duration output, the current note pitch data, the current note musical context data, the current note duration data, and duration and pitch information stored in state units associated with the note generation part;
• and
• feedback process steps arranged to feed the pitch and duration of the subsequent note back to the rhythm generation and note generation parts, the subsequent note thereby becoming the current note for a following iteration.

A system for automatically generating music on the basis of an initial note sequence input, the system including:

• a score interpreter for interpreting each note in the initial input sequence, thereby to generate current note pitch data, current note duration data and current note musical context data;
• a rhythm production recurrent artificial neural network for generating a subsequent note duration output on the basis of the current note duration data, the current musical context data and note duration information stored in state units associated with the rhythm production recurrent artificial neural network;
• a note generation recurrent artificial neural network for generating a subsequent note on the basis of the subsequent note duration output, the current note pitch data, the current note musical context data, the current note duration data, and duration and pitch information stored in state units associated with the note generation recurrent artificial neural network;
• and
• feedback means for feeding the pitch and duration of the subsequent note back to the rhythm generation and note generation recurrent artificial neural networks, the subsequent note thereby becoming the current note for a following iteration;
• wherein during a learning phase each of the recurrent artificial neural networks is trained by feeding the score of at least one piece of music through the score interpreter, internal weights associated with an artificial neural network portion of each of the recurrent artificial neural networks being adjusted in response to the input musical score.

A method for automatically generating music on the basis of an initial note sequence input, the method comprising steps of:

• interpreting each note in the initial input sequence, thereby to generate current note pitch data, current note duration data and current note musical context data;
• generating a subsequent note duration output on the basis of the current note duration data and the current note context data using a rhythm production recurrent artificial neural network;
• storing the current musical context data and note duration information in one or more state units associated with the rhythm production recurrent artificial neural network;
• generating a subsequent note using a note generation recurrent artificial neural network on the basis of the subsequent note duration output, the current note pitch data, the current note musical context data, the current note duration data, and duration and pitch information stored in state units associated with the note generation recurrent artificial neural network;
• and
• feeding back the pitch and duration of the subsequent note back to the rhythm generation and note generation recurrent artificial neural networks, the subsequent note thereby becoming the current note for a following iteration;
• wherein during a learning phase each of the recurrent artificial neural networks is trained by feeding the score of at least one piece of music through the score interpreter, internal weights associated with an artificial neural network portion of each of the recurrent artificial neural networks being adjusted in response to the input musical score.

A computer program product including a computer readable medium having recorded thereon a computer program for automatically generating music on the basis of an initial note sequence input, the computer program comprising:

• interpretation process steps arranged to interpret each note in the initial input sequence, thereby generating current note pitch data, current note duration data, and current note musical context data;
• generating process steps arranged to generate a subsequent note duration output on the basis of the current note duration data and the current note context data using a rhythm production recurrent artificial neural network;
• storing process steps arranged to store the current musical context data and note duration information one or more state units associated with the rhythm production recurrent artificial neural network;
• generation process steps arranged to generate a subsequent note using a note generation recurrent artificial neural network on the basis of the subsequent note duration output, the current note pitch data, the current note musical context data, the current note duration data, and duration and pitch information stored in state units associated with the note generation recurrent artificial neural network;
• and
• feedback process steps arranged to feed the pitch and duration of the subsequent note back to the rhythm generation and note generation recurrent artificial neural networks, the subsequent note thereby becoming the current note for a following iteration;
• wherein during a learning phase each of the recurrent artificial neural networks is trained by feeding the score of at least one piece of music through the score interpreter, internal weights associated with an artificial neural network portion of each of the recurrent artificial neural networks being adjusted in response to the input musical score.