LAGNet Speech Separation With Local-Global Sequence Modeling
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Solution Overview
Problem
Existing speech separation models face challenges in effectively processing long sequences in the time domain, requiring substantial computational resources and failing to adequately model both local and global information, leading to inefficiencies in speech separation performance.
Innovation Solution
A deep learning-based speech separation model called LAGNet, which employs a multi-scale sequence model with a local encoder that progressively compresses sequences using one-dimensional convolution-based local modeling blocks, a bottleneck utilizing multiple self-attention-based global modeling blocks, and a decoder that progressively reconstructs the sequence using local modeling blocks, along with skip connections for efficient information processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dual-path sequence model (DPRNN or Sepformer) is used to process long sequences, then global modeling capability is improved, but computational resources required increase substantially
Solution Approach 1:
The patent segments the sequence processing into two distinct paths: a first path that processes local information within chunks using TCN, and a second path that processes global information across chunks using attention mechanisms. This segmentation allows each path to be optimized for its specific function, reducing overall computational complexity while maintaining both local and global modeling capabilities.
Solution Approach 2:
The patent introduces a chunking dimension that divides the sequence into multiple chunks, allowing local processing within chunks and global processing across chunks. This dimensional approach enables efficient hierarchical processing where local patterns are captured first, then global dependencies are modeled, reducing the computational burden compared to processing the entire sequence globally.
2Device complexity
If multi-scale sequence model (SuDoRM-RF) is used to reduce computation, then computational resources are reduced, but global information modeling effectiveness decreases
Solution Approach 1:
The patent segments sequence processing into local and global paths, where the global path specifically addresses global information modeling using attention mechanisms that operate across chunk boundaries. This ensures that global dependencies are explicitly captured without requiring substantial computation across the entire sequence.
Solution Approach 2:
The patent adds a global processing dimension that operates on top of local chunk processing. The global path uses attention mechanisms to model long-range dependencies across chunks, effectively capturing global information while keeping computational costs manageable through the hierarchical chunking structure.
3Productivity
If one-dimensional convolution-based local modeling is used, then local information processing is efficient, but global sequence information is not effectively modeled
Solution Approach 1:
The patent segments the modeling task into two complementary paths: a local path using one-dimensional convolutions for efficient local pattern recognition, and a global path using attention mechanisms for capturing long-range dependencies. This segmentation allows each path to leverage its strengths without compromising the other.
Solution Approach 2:
The patent merges the outputs of the local TCN path and the global attention path to produce the final sequence representation. This combination integrates both local and global information, allowing the model to benefit from the efficiency of convolutional local processing while incorporating global contextual understanding from the attention mechanism.
Data Source
AI summary
A method and an apparatus for separating speeches based on local and global modeling network (LAGNet) are provided. A speech separation apparatus includes an encoder configured to progressively compress a sequence of mixed speeches by using a one-dimensional convolution-based local block to generate local information, a bottleneck configured to generate global information by using a multiple self attention-based global block, and a decoder configured to progressively reconstruct the sequence by using the local block. A speech separation apparatus also utilizes a skip connection that includes gates configured to filter the local information by using the global information.