## 3 Proposed Work
### Feature Extraction  
In speech feature extraction, this paper adopts the MFCC, which is the most common and effective algorithm for extracting sound features based on the principle of Cepstrum, which is more suitable for human hearing. In the calculation of the MFCC, the first step is to pre-aggravate the high frequency part of the speech signal to smooth the signal of the whole channel from low to high frequency, and at the same time, to eliminate the influence of the speaker in the process of occurrence. The continuous speech signal is divided into frames; each frame is multiplied by a Hamming window to improve the continuity between the left and right ends of the frame; after that, it needs to be converted into the energy distribution in the frequency domain, so that the transformation of speech signal in the time domain can more clearly express the characteristics of the signal, that is, the Fourier transformation process, so as to obtain the spectrum map distributed in different time windows on different time axes, as well as the analysis of gene frequency and formant. The pre-processing work is performed on the audio in the dataset, because the audio length in the dataset is distributed around 4 s. In this experiment, the random offset/padding method is used to unify the audio lengths. Additionally, that is, a random window of 4 s is selected for audio longer than 4 s, and the excess is discarded, and a random back-and-forth zero complement is performed for audio shorter than 4 s. In this experiment, the sampling rate of audio is 16 k, so the length of the input is set to 16,000 \(\times\) 4, which is the length of the audio after unification. To start the feature extraction of audio, this experiment uses Librosa's audio manipulation toolkit, which provides a very powerful and easy way to manipulate audio. In the extraction of the previously mentioned features, Librosa is used to extract the MFCC, log-Mel and STFT features since the original waveform does not require much transformation and can be obtained directly. In terms of parameter settings, the MFCC is set to a MFCC of 40, frame shift of 320, and sampling rate of 16 k.  
Figure 1: The overall framework of proposed TCDCN model. The Mel filter of the Mel spectrum is set to 128. After extracting several features needed for the experiment, the extracted features are saved as feature files. The subsequent project is the training network part. For model building and training, this thesis uses the TensorFlow framework. There are three main reasons for using TensorFlow: the easy-to-use API, Python support, and dynamic computational graphs. Given the pre-trained Transformer network structure, we only select the most probable sound events to extract embedded features. We think \(n=(n_{1},n_{2},n_{3},\ldots,n_{M})\) is a digital vector, where \(M\) is the number of sound events defined in the UrbanSound8K dataset, and \(n_{\text{i}}\) is the total number of times that the \(i\)-th sound event is detected in each program and used to mark the clip. The average embedding feature is calculated as \(\overline{n_{\text{i}}}=\frac{n_{\text{i}}}{\sum_{i=1}^{M}n_{\text{i}}}\). Given the embedding average, the baseline system divides them into 10 types. In order to make our model more robust on small datasets and remove the noise contained in the original dataset, we reduce the dimensionality of the features. We use supervised and unsupervised forms such as Principal Component Analysis (PCA) and LDA to reduce the dimensionality of the data. It is found that the Stochastic Neighbor Embedding (SNE) algorithm has an obvious division effect on the dimensionality reduction in high-dimensional data. The high-dimensional data are represented by \(X\), \(Xi\) is the \(i\)-th sample, and \(Y\) is the low-dimensional data.