Robust Active Speaker Detection in Noisy Environments
Core Concepts
A novel framework that utilizes audio-visual speech separation as guidance to learn noise-free audio features for robust active speaker detection in noisy environments.
Abstract
The paper addresses the issue of active speaker detection (ASD) in noisy environments and proposes a robust active speaker detection (rASD) framework. Existing ASD approaches leverage both audio and visual modalities, but non-speech sounds in the surrounding environment can negatively impact performance.
The key highlights of the proposed framework are:
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It utilizes audio-visual speech separation as guidance to learn noise-free audio features for ASD. The speech separator and ASD model are jointly optimized in an end-to-end manner.
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It introduces a dynamic weighted loss approach to handle inherent noise in speech sounds and further enhance the robustness of audio features.
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It collects a real-world noise audio (RNA) dataset to facilitate investigations on the impact of non-speech sounds on ASD.
Experiments demonstrate that non-speech audio noises significantly impact ASD models, and the proposed framework can improve ASD performance in noisy environments. The framework is general and can be applied to different ASD approaches to improve their robustness.
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Robust Active Speaker Detection in Noisy Environments
Stats
Non-speech sounds can significantly degrade active speaker detection performance, with performance drops of up to 19.3% mAP when noise level α = 1.
Naive cascaded approaches that first perform speech separation and then use the separated speech as input for ASD can only provide marginal improvements, limited by residual noise and quality degradation in the separated speech.
Quotes
"To overcome this, we propose a novel framework that utilizes audio-visual speech separation as guidance to learn noise-free audio features."
"We also collected a real-world noise audio dataset to facilitate investigations."
"Experiments demonstrate that non-speech audio noises significantly impact ASD models, and our proposed approach improves ASD performance in noisy environments."
Deeper Inquiries
How can the proposed framework be extended to handle more complex audio-visual scenarios, such as multiple active speakers or overlapping speech
The proposed framework can be extended to handle more complex audio-visual scenarios by incorporating advanced techniques for multi-speaker detection and speech separation. One approach could be to modify the speech separator to handle multiple speakers by incorporating additional output channels or modifying the architecture to handle overlapping speech. This would involve training the model on datasets with multiple speakers and developing mechanisms to separate and identify each speaker's speech in the presence of overlapping audio. Additionally, integrating speaker diarization techniques could help in identifying and tracking multiple speakers in a video frame. By enhancing the robust feature generation module to extract features specific to each speaker, the framework can be adapted to detect and differentiate between multiple active speakers in noisy environments.
What are the potential limitations of the dynamic weighted loss approach, and how can it be further improved to handle a wider range of inherent speech noises
The dynamic weighted loss approach, while effective in handling inherent speech noises, may have limitations in scenarios where the noise characteristics are diverse or complex. One potential limitation is the reliance on accurate noise labels for training the weight generator, which may not always be available or may be subjective. To address this limitation, the weight generator can be enhanced by incorporating self-supervised learning techniques to learn noise patterns directly from the audio data. Additionally, exploring adaptive weighting strategies based on the audio content or incorporating reinforcement learning to dynamically adjust weights during training could improve the model's ability to handle a wider range of inherent speech noises. Regularization techniques can also be applied to prevent overfitting and enhance the generalization of the weight generator across different noise types.
Given the advancements in audio-visual understanding, how can the insights from this work be applied to other related tasks, such as audio-visual event detection or audio-visual scene understanding
The insights from this work can be applied to other related tasks in audio-visual understanding, such as audio-visual event detection and audio-visual scene understanding. For audio-visual event detection, the framework's approach to learning noise-free audio features can be leveraged to improve event detection models' robustness in noisy environments. By incorporating audio-visual fusion techniques and dynamic weighted loss mechanisms, event detection models can effectively identify and classify events in videos with complex audio backgrounds. Similarly, in audio-visual scene understanding, the framework's multi-task learning approach and speech separation guidance can enhance scene analysis models' performance by extracting clean audio features for scene classification and context recognition. By adapting the framework's principles to these tasks, researchers can develop more reliable and accurate audio-visual systems for various applications.
