# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 1 Introduction  
Speech separation involves the processing of audio signals in order to separate multiple speech signals that overlap, with the objective of identifying and processing each speaker's speech separately. speech separation is very beneficial in diverse interference conditions, such as while recording conferences, making phone conversations, or mixing music. It allows us to isolate distinct speech outputs and solve the problem of speaker overlap. Effective speech separation methods usually involve a comprehensive understanding of signal processing, machine learning, and artificial intelligence. Deep neural network (DNN) techniques have greatly improved state-of-the-art speech separation approaches. To enhance the functionality of speech separation systems, the state-of-the-art methods employ a variety of DNN designs and training techniques [1], including the following. * Deep clustering-based methods [2, 3] learn embeddings from mixed audio signals that can be used to distinguish between different speakers. In [2], the spectrogram embeddings are learned through a DNN to be discriminative for clustering, and thework in [3] extends the deep clustering framework by end-to-end training with better regularization, longer temporal context, and a deeper structure. * Time-Domain Audio Separation Network (TasNet) [4; 5] circumvents the drawbacks of time-frequency representation by operating directly in the time domain to separate speech signals. TasNet employs a 1D convolutional encoder-decoder system, with the separation module as its fundamental component. This module is responsible for estimating source masks based on the encoder outputs. These masks are employed to measure the encoded mixed signal in order to separate different sources. * The ability to capture longer contextual information has been greatly improved with the rise of self-attention mechanisms and transformer design [6], leading to even better speech separation quality. Sepformer [7] and DPTNet [8] are two examples of speech separation systems that adopt transformers. They are typically implemented in the dual-path architecture, which introduces a large number of parameters due to the fact that these systems model intra-chunk local features and inter-chunk global relationships separately. Several studies have developed more efficient speech separation architectures to address the growing concern over large models with significant computing demands. For example, the dual-path recurrent neural network (DRPNN) [9] is a lightweight speech separation model that efficiently models long sequences in time for a single channel. DPRNN splits the sequence into smaller chunks and, thus, allows for better gradient flow and easier optimization. This structure also enables the network to capture both local and global dependencies in the sequence. Another study [10] uses a BLSTM-based binary masking module to create a comparable lightweight speech separation system. This masking module is designed to separate the sound source of interest (SOI) from the cumulative environmental interference. Furthermore, TDANet [11] simulates the human brain's top-down attention mechanism by performing speech separation using global attention and cascaded local attention layers, providing comparable performance to Sepformer but with significantly less computational complexity. In addition, SuDoRM-RF [12] and its derivative SuDoRM-RF++ [13] use successive depth-wise convolutional downsampling to extract multi-resolution temporal features, reducing the number of layers needed while effectively capturing long-term temporal relationships for speech separation. This study presents a new network called ESC-MASD-Net to improve the performance of speech separation models. This network is based on the SuDoRM-RF++ architecture but with two additional modules: the residual conformer network (ResCon) and the multi-view attention (MA) network. These two modules are crucial elements of the MANNER network [14].