Audio Stem Identification via Machine Learning Probability Vectors
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Solution Overview
Problem
Current methods fail to effectively identify which audio stems in a database have been used to create a music track, especially as the database grows, as users cannot listen to all stems to determine their usage.
Innovation Solution
A method using machine learning to train a model on audio stems from multiple songs, predicting stems with a probability of being related to input stems, and employing audio similarity algorithms to find similar stems, along with lead sheet and chord sequence analysis, to identify complementary stems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually listen to all stems in the database to determine usage, then identification accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent replaces the mechanical/manual listening process with an automated audio analysis system that uses signal processing and machine learning algorithms to identify stems programmatically, eliminating the need for human listeners to manually evaluate each stem
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between the stem database and users, automatically determining stem usage through audio feature extraction and comparison without requiring direct user involvement in the evaluation process
2Adaptability or versatility
If the stem database grows larger to provide more options, then stem selection versatility is improved, but identification complexity increases
Solution Approach 1:
The patent segments the complex identification task into distinct processing stages including audio feature extraction, similarity computation, and ranking, allowing the system to handle large databases efficiently by breaking down the overall complexity into manageable components
Solution Approach 2:
The patent transforms the identification problem from a complex qualitative assessment into a quantitative parameter-based system using audio features such as spectral characteristics, temporal patterns, and statistical measures, enabling efficient comparison and ranking of stems regardless of database size
Data Source
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AI summary
Methods, systems and computer program products are provided for identifying an audio stem. Audio stems (t1, .., tN) are stored on a stem database and songs (S1, ..., SP) made with at least a subset of the plurality of the audio stems (t1, .., tN) are stored on a song database. At least partially composed song (S∗) having a predetermined number of pre-selected stems (k) are received. In turn, a probability vector (or relevance value or ranking) is produced for each stem (t1, .., tN) to be complementary to the at least partially composed song (S∗).