# A Deep Attention Model for Environmental Sound Classification from Multi-Feature Data
## 1 Introduction  
In recent years, environmental sound identification technology has been paid more and more attention due to its obvious engineering potential. This technology is chiefly applied to recognizing specific sound events, such as engine sound, rain sound, and baby crying, to achieve intelligent information interaction between environmental sound and the computer. It has extensive applications in robot hearing systems [1], smart homes [2], and audio monitoring systems [3], among others. For the traditional environmental sound recognition, feature vectors are manually extracted, such as Mel Frequency Cepstral Coefficients (MFCC), Mel spectrogram feature, wavelet transform, etc., and then feature classification is completed by machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor algorithm (KNN), matrix factorization, and Extreme Learning Machine (ELM) [4; 5; 6; 7; 8; 9; 10]. These methods are not only cumbersome, but are also of poor generalizability and pose stringent requirements on datasets, making it hard to deploy them in practical applications. In recent years, data-driven deep learning methods are developing fast and are widely used in image fusion, target detection, and gesture recognition [11]. Deep learning achieves complex function approximation through nonlinear mapping and demonstrates its powerful ability to extract essential features of datasets from a few sample sets. The environmental sound recognition based on deep learning has replaced the traditional manual feature extraction methods to become the mainstream research direction. According to the type of neural networks, sound recognition methods can be divided into the following two categories: environmental sound recognition based on one-dimensional convolution (1D CNN) neural network [12; 13; 14; 15] and environmental soundrecognition based on two-dimensional convolution neural network (2D CNN) [16; 17; 18; 19; 20]. As audio sequences are generally long and the number of sampling points is large, audio sequences will be selected as input using a moving window for using 1D CNN, which is relatively complex and prone to the influence of noise. If two-dimensional convolution is employed to extract environmental sound features, the audio signal will be pretreated so that the one-dimensional sound signal is mapped to a two-dimensional spectrogram. In this process, the impact of noise on the entire system is mitigated, but the phase information in the sound signal is ignored, and a lot of experiments are necessary to determine the parameters in signal mapping.  
To cope with noise interference and loss of phase information, this paper proposes Log-Mel spectrogram, time-frequency spectrogram, and phase spectrogram as the input of the recognition network to supplement the phase information of the sound signal and a time-frequency attention mechanism be introduced into the input part of the network to suppress the interference from irrelevant frequency bands and background noise in the audio. The proposed method has been verified on three commonly used datasets and achieves good performance.