9 accepted papers
Capturing time-frequency patterns along the frequency axis, is crucial for the precision of sound event detection systems. Frequency dynamic convolution (FDY) and a series of its variants which incorporate frequency-adaptive kernels in standard 2D convolutions, have demonstrated remarkable performan…
Beamforming weights prediction via deep neural networks has been one of the main methods in multi-channel speech enhancement tasks. The spectral-spatial cues are crucial in beamforming weights estimation, however, many existing works fail to optimally predict the beamforming weights with an absence…
Acoustic echo cancellation is a key issue in hand-free communication systems. In this paper, we proposed a hybrid signal processing and deep echo cancellation method, where a two-stage neural network is designed to remove residual echo progressively. For the personalized acoustic echo cancellation,…
Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech. Previous works introduce the speaker embedding into speech enhancement models by means of concatenation or affine transformation. In this paper, we propose a speaker attentive…
This paper describes our topic segmentation (TS) system submitted to the ICASSP2023 Signal Processing Grand Challenge - General Meeting Understanding and Generation challenge (MUG). We make three improvements to the official baseline system of the TS track. Firstly, considering that meeting transcri…
In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise divide-and-conquer fashion. Specifically, in the first stage, the speech improvement network focuses on recovering the miss…
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two C…
The classification framework has been popularly adopted to perform sound event detection. However, the existing neural network based classification based approaches treat each feature dimension equally and the varying influence of feature dimensions has not been taken into consideration. To deal wit…
Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt the multi-label classification technique to perform the polyphonic acoustic event detection with a global threshold to d…