Localized Key Finding from Audio Using Nonnegative Matrix Factorization for Segmentation
نویسنده
چکیده
A model for localized key finding from audio is proposed. Besides being able to estimate the key in which a piece starts, the model can also identify points of modulation and label multiple sections with their key names throughout a single piece. The front-end employs an adaptive tuning stage prior to spectral analysis and calculation of chroma features. The segmentation stage uses groups of contiguous chroma vectors as input and identifies sections that are candidates for unique local keys in relation to their neighboring key centers. Nonnegative matrix factorization with additional sparsity constraints and additive updates is used for segmentation. The use of segmentation is demonstrated for single and multiple key estimation problems. A correlational model of key finding is applied to the candidate segments to estimate the local keys. Evaluation is given on three different data sets and a range of analysis parameters.
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