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This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.