# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 5 DC AMR Detection System  
Distinguishing between SC and DC speech signals constitutes a binary classification task due to the presence of only two possible classes. As a result, previous research predominantly relied on traditional pattern recognition techniques such as support vector machines (SVM) to tackle this challenge. However, the effectiveness of such methods hinges on pre-processing the speech signals to extract pertinent information for the given task. It is imperative that the extracted features are highly representative to ensure reasonable performance. In earlier studies, feature extraction often involved the use of handcrafted fea  
Figure 3: Average difference phase spectra between the original (uncompressed) and SC speech signals (**left**) and between the SC and DC speech signals (**right**). The AMR-WB codec is used to generate the SC and DC signals. For the DC signals, the first compression bit rate (BR1) matches with the compression bit rate of the SC speech signal (BR = 8.85 kbps) to asses the effect of second compression bit rate (BR2) on DC signals. tures, leveraging prior knowledge about the compression algorithm. For instance, features derived from MDCT coefficients were utilized for MP3 and AAC codecs, while feature vectors obtained through LP analysis were employed for the AMR codec. Subsequently, these extracted features were fed into classification algorithms to either estimate the distributions of training features (generative models) or draw a decision boundary between the two classes in the feature space (discriminative models). In the case of generative models, decisions are made based on the posterior probabilities of potential classes given the test feature vector. Conversely, discriminative models employ distance measures to make decisions. Nevertheless, traditional pattern classification techniques suffer from certain limitations, notably the challenge of determining which features are most pertinent for the task and selecting an appropriate classification method. These decisions often necessitate profound domain expertise and entail a protracted trial-and-error process. With the rapid advancement of deep learning methodologies, the concept of end-to-end learning has emerged. In end-to-end learning, DNN possesses the capability to automatically learn the most pertinent features and make decisions based on input data. Consequently, both feature extraction and classification tasks are seamlessly integrated into the DNN system. In this study, we leverage LSTM-based DNNs for the task of DC AMR speech detection. LSTM networks, belonging to the family of recurrent neural networks (RNNs), are renowned for their ability to capture long-term dependencies. These networks comprise a series of memory blocks that are interconnected recurrently. They acquire knowledge of long-term dependencies by retaining previous information within their memory blocks and utilizing it to compute the output of the current block based on both previous and current input. Hence, LSTM networks are well-suited for modeling temporal sequences, making them particularly appropriate for tasks involving time-series data such as spectrograms and phase features extracted using STFT. These features inherently represent a time-series sequence of frequency domain features, rendering LSTM networks suitable for learning the long-term dependencies essential for DC AMR speech detection. In this study, we propose two different LSTM-based systems for DC speech detection. The baseline LSTM system, illustrated in Figure 4, comprises two LSTM layers, followed by one fully connected layer and a classification layer. Each LSTM layer encompasses 256 units with tanh nonlinearity, while the fully connected (FC) layer consists of 512 units with a sigmoid activation function. The classification layer employs the softmax activation function, featuring two units corresponding to the SC and DC speech classes, respectively.