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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.

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.

In this paper, we propose an adaptive STFT (ASTFT) with adaptive chirp-modulated Gaussian window. The window is obtained from rotating Gaussian function in time-frequency plane by fractional Fourier transform (FRFT). It is completely adaptive where the two parameters, FRFT rotation angle and Gaussian variance, are signal-dependent. The angle dependents on the chirp rate of the signal. The variance is determined by the chirp rate and its first derivative.

In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure.
By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This approach is able to deal with the lack of the necessary statistical information for an optimal adjustment of the filter and its structure.

Performance estimation is crucial to the assessment of novel algorithms and systems. In detection error trade-off (DET) diagrams, discrimination performance is solely assessed targeting one application, where cross-application performance considers risks resulting from decisions, depending on application constraints. For the purpose of interchangeability of research results across different application constraints, we propose to augment DET curves by depicting systems regarding their support of security and convenience levels.