  This paper proposes an noise type classification aided attention-based neural
network approach for monaural speech enhancement. The network is constructed
based on a previous work by introducing a noise classification subnetwork into
the structure and taking the classification embedding into the attention
mechanism for guiding the network to make better feature extraction.
Specifically, to make the network an end-to-end way, an audio encoder and
decoder constructed by temporal convolution is used to make transformation
between waveform and spectrogram. Additionally, our model is composed of two
long short term memory (LSTM) based encoders, two attention mechanism, a noise
classifier and a speech mask generator. Experiments show that, compared with
OM-LSA and the previous work, the proposed noise classification aided
attention-based approach can achieve better performance in terms of speech
quality (PESQ). More promisingly, our approach has better generalization
ability to unseen noise conditions.
