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In recent years, much speech separation research has focused primarily on improving model performance.
However, for low-latency speech processing systems, computational efficiency is equally critical.
In this paper, we propose a speech separation model with significantly reduced parameter size and
computational cost:
Time-Frequency Interleaved Gain Extraction and Reconstruction Network (TIGER).
TIGER leverages prior knowledge to divide frequency
bands and applies compression on frequency information.
We employ a multi-scale selective attention (MSA) module to extract contextual features,
while introducing a full-frequency-frame attention (F^3A) module to capture both temporal and
frequency contextual information.
Additionally, to more realistically evaluate the performance of speech separation models in complex
acoustic environments,
we introduce a novel dataset called EchoSet. This dataset includes noise and more realistic
reverberation
(e.g., considering object occlusions and material properties), with speech from two speakers overlapping
at random proportions.
Experimental results demonstrated that TIGER significantly outperformed
state-of-the-art (SOTA) model TF-GridNet
on the EchoSet dataset in both inference speed and separation quality, while reducing the number of
parameters by 94.3%
and the MACs by 95.3%. These results indicate that by utilizing frequency band-split and
interleaved modeling structures,
TIGER achieves a substantial reduction in parameters and computational costs while maintaining high
performance.
Notably, TIGER is the first speech separation model with fewer than 1 million parameters
that achieves performance close to the SOTA model.
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