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Audio processors whose parameters are modified periodically
over time are often referred as time-varying or modulation based
audio effects. Most existing methods for modeling these type of
effect units are often optimized to a very specific circuit and cannot
be efficiently generalized to other time-varying effects. Based on
convolutional and recurrent neural networks, we propose a deep
learning architecture for generic black-box modeling of audio processors
with long-term memory. We explore the capabilities of

Inspired by human hearing perception, we propose a twostage multi-resolution end-to-end model for singing melody extraction in this paper. The convolutional neural network (CNN) is the core of the proposed model to generate multiresolution representations. The 1-D and 2-D multi-resolution analysis on waveform and spectrogram-like graph are successively carried out by using 1-D and 2-D CNN kernels of different lengths and sizes.

Environmental sound classification (ESC) is usually conducted based on handcrafted features such as the log-mel feature. Meanwhile, end-to-end classification systems perform feature extraction jointly with classification and have achieved success particularly in image classification. In the same manner, if environmental sounds could be directly learned from the raw waveforms, we would be able to extract a new feature effective for classification that could not have been designed by humans, and this new feature could improve the classification performance.

This is oral presentation at ISCSLP, for more information, please refer to paper:

Jun-Hua Liu, Zhen-Hua Ling, Si Wei, Guo-Ping Hu, Li-Rong Dai, "Cluster-Based Senone Selection for the Efficient Calculation of Deep Neural Network Acoustic Models", ISCSLP, 2016.

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each regressor estimates the onset and offset positions of the target event. The estimation confidence scores output by a regressor are then used to quantify how the target event aligns with the temporal structure of the corresponding category.