  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.
