Temporal Convolution Network Based Onset Detection and Query by Humming System Design

Onsets are a key factor to split audio into several notes. In this paper, we ensemble multiple temporal convolution network (TCN) based model and utilize a restricted frequency range spectrogram to achieve more robust onset detection. Different from the present onset detection of QBH system which is only available in a clean scenario, our proposal of onset detection and speech enhancement can prevent noise from affecting onset detection function (ODF). Compared to the CNN model which exploits spatial features of the spectrogram, the TCN model exploits both spatial and temporal features of the spectrogram. As the usage of QBH in noisy scenarios, we apply the TCN-based speech enhancement as a preprocessor of QBH. With the combinations of TCN-based speech enhancement and onset detection, simulations show that the proposal can enable the QBH system in both noisy and clean circumstances with short response time.

Pitch Estimation by Denoising Preprocessor and Hybrid Estimation Model

Pitch estimation is to estimate the fundamental frequency and the midi number and plays a critical role in music signal analysis and vocal signal processing. In this work, we proposed a new architecture based on a learning-based enhancement preprocessor and a combination of several traditional and deep learning pitch estimation methods to achieve better pitch estimation performance in both noisy and clean scenarios. We test 17 different types of noise and 4 SNRdb noise levels. The results show that the proposed pitch estimation can perform better in both noisy and clean scenarios with short response time.