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20242026
most citedSpectrogram features for audio and speech analysis

7 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2026★ 7 cited

Spectrogram features for audio and speech analysis

Ian McLoughlin, Lam Pham, Yan Song +7

Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the…

cs.SD2025

An Efficient Transfer Learning Method Based on Adapter with Local Attributes for Speech Emotion Recognition

Haoyu Song, Ian McLoughlin, Qing Gu +2

Existing speech emotion recognition (SER) methods commonly suffer from the lack of high-quality large-scale corpus, partly due to the complex, psychological nature of emotion which…

cs.SD2025

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries

Pengfei Cai, Yan Song, Qing Gu +3

Most existing sound event detection~(SED) algorithms operate under a closed-set assumption, restricting their detection capabilities to predefined classes. While recent efforts hav…

cs.SD2024

Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection

Pengfei Cai, Yan Song, Nan Jiang +2

A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs.…

cs.SD2024★ 1 cited

USTC-KXDIGIT System Description for ASVspoof5 Challenge

Yihao Chen, Haochen Wu, Nan Jiang +13

This paper describes the USTC-KXDIGIT system submitted to the ASVspoof5 Challenge for Track 1 (speech deepfake detection) and Track 2 (spoofing-robust automatic speaker verificatio…