Method and system for hybrid AI-based song construction

1. A method of hybrid AI/expert engine-based song generation, wherein is provided an audio loop database containing a plurality of audio loops, each of said audio loops having a plurality of performance parameters associated therewith, comprising the steps of:

(a) requiring a user to select a music style from among a plurality of music styles

(b) requiring the user to select at least one initial song part different from any audio loop in said audio loop database;

(c) using an expert engine to automatically analyze said selected at least one song part to produce a song skeleton structure based on said selected music style and said at least one song part, said song skeleton structure comprising a plurality of empty skeleton song parts, each of said empty skeleton song parts being associated with one or more expert parameters provided by said expert system;

(d) adding said user provided song part to at least one of said empty skeleton song parts in said song skeleton structure;

(e) for each of said skeleton song parts not containing said user provided song part, using an AI system to select a plurality of audio loops from said audio loop database to add to said each of said empty skeleton song parts in said song skeleton structure, said AI system selecting said plurality of said audio loops using at least said plurality of performance parameters associated with each audio loop in said database together with said expert parameters associated with each of said skeleton song parts;

(f) for each of said skeleton song parts not containing said user provided song part, adding said selected audio loops to each of said empty skeleton song part in said song skeleton structure, thereby generating a music work containing at least one of said at least one song parts selected by the user and at least one of said at least one skeleton song parts containing only audio loops added by said AI system, said song skeleton structure comprising a hybrid AI/expert engine-based song; and

(g) performing at least a portion of said hybrid AI/expert engine-based song for the user.

2. The method according to claim 1 , wherein said at least one initial song part selected by the user comprises a plurality of different recorded instruments.

3. The method according to claim 1 , wherein said expert parameters comprise a collection of rules associated with each provided music style.

4. The method according to claim 1 , wherein step (e) comprises the steps of:

(e1) using said audio loops in said audio loop database and said performance parameters associated therewith to train said AI system, and

(e2) for each of said skeleton song parts not containing said user provided song part, using an AI system to select a plurality of audio loops from said audio loop database to add to said each of said empty skeleton song parts in said song skeleton structure, said AI system selecting said plurality of said audio loops using at least said plurality of performance parameters associated with each audio loop in said database together with said expert parameters associated with each of said skeleton song parts.

5. The method according to claim 1 , wherein said skeleton structure comprises at least a skeleton structure song length, a skeleton structure song style, a plurality of skeleton structure song part types, a plurality of skeleton structure instruments, at least one skeleton structure harmony sequence, a skeleton structure, a dynamics dramaturgy and a skeleton structure variance setting.

6. The method according to claim 5 , wherein said variance setting comprises a permitted level of diversity of an AI selected loop with respect to any previously inserted audio loops.

7. The method according to claim 1 , wherein said expert parameters comprise one or more of:

(i) a runtime at a given tempo,

(ii) a song part style designation,

(iii) one or more permitted instruments,

(iv) a harmonic progression,

(v) a chord change progression,

(vi) a variance setting,

(vii) a dynamics dramaturgy, and

(viii) a percussion pattern.

8. A method of hybrid AI/expert engine-based song generation, wherein is provided an audio loop database containing a plurality of audio loops, each of said audio loops having a plurality of performance parameters associated therewith, comprising the steps of:

(a) requiring a user to select a music style from among a plurality of music styles

(b) requiring the user to select at least one initial song part different from any audio loop in said audio loop database;

(c) using an expert engine to automatically analyze said selected at least one song part to produce a song skeleton structure based on said selected music style and said at least one song part, said song skeleton structure comprising a plurality of empty skeleton song parts, each of said empty skeleton song parts being associated with one or more expert parameters provided by said expert system, wherein said expert parameters comprise one or more of:

(i) a runtime at a given tempo,

(ii) a song part style designation,

(iii) one or more permitted instruments,

(iv) a harmonic progression,

(v) a chord change progression,

(vi) a variance setting,

(vii) a dynamics dramaturgy, and

(viii) a percussion pattern;

(d) adding said user provided song part to at least one of said empty skeleton song parts in said song skeleton structure;

(e) for each of said skeleton song parts not containing said user provided song part, using an AI system to select a plurality of audio loops from said audio loop database to add to said each of said empty skeleton song parts in said song skeleton structure, said AI system selecting said plurality of said audio loops using at least said plurality of performance parameters associated with each audio loop in said database together with said expert parameters associated with each of said skeleton song parts;

(f) for each of said skeleton song parts not containing said user provided song part, adding said selected audio loops to each of said empty skeleton song part in said song skeleton structure, thereby generating a music work containing at least one of said at least one song parts selected by the user and at least one of said at least one skeleton song parts containing only audio loops added by said AI system, said song skeleton structure comprising a hybrid AI/expert engine-based song; and

(g) performing at least a portion of said hybrid AI/expert engine-based song for the user.

9. The method according to claim 8 , wherein said at least one initial song part selected by the user comprises a plurality of different recorded instruments.

10. The method according to claim 8 , wherein said expert parameters comprise a collection of rules associated with each provided music style.

11. The method according to claim 8 , wherein step (e) comprises the steps of:

(e1) using said audio loops in said audio loop database and said performance parameters associated therewith to train said AI system, and

(e2) for each of said skeleton song parts not containing said user provided song part, using an AI system to select a plurality of audio loops from said audio loop database to add to said each of said empty skeleton song parts in said song skeleton structure, said AI system selecting said plurality of said audio loops using at least said plurality of performance parameters associated with each audio loop in said database together with said expert parameters associated with each of said skeleton song parts.

12. The method according to claim 8 , wherein said skeleton structure comprises at least a skeleton structure song length, a skeleton structure song style, a plurality of skeleton structure song part types, a plurality of skeleton structure instruments, at least one skeleton structure harmony sequence, a skeleton structure, a dynamics dramaturgy and a skeleton structure variance setting.

13. The method according to claim 8 , wherein said variance setting comprises a permitted level of diversity of an AI selected loop with respect to any previously inserted audio loops.