21 accepted papers
Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments in video and audio, offering strong interpretability for security and forensics. While recent State Space Models (SSMs) show promise in precise temporal reasoning, their use in TFL is hindered by ambiguous boundaries
Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within videos or audio streams, providing interpretable evidence for multimedia forensics and security. While most existing TFL methods rely on dense frame-level labels in a fully supervised manner, Weakly Supervised
Existing end-to-end neural codecs have made great progress in preserving audio quality. Despite their success, they still face challenges in achieving accurate and efficient quantization. Specifically, these codecs often overlook which features have a greater impact on perceptual audio quality durin…
Current deep neural networks for speech enhancement (SE) aim to minimize the distance between the output signal and the clean target by filtering out noise features from input features. However, when noise and speech components are highly similar, SE models struggle to learn effective discrimination…
Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes…
Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objecti…
Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additi…
Speech enhancement is a vital and highly ill-posed problem for many speech downstream tasks. While currently existing deep learning based speech enhancement methods have held state-of-the-art results, they still possess apparent shortcomings in that most of the deep learning based models lack interp…
Existing deep learning-based speech enhancement methods only adopt clean speech as positive samples to guide the training of speech enhancement networks while negative samples, i.e., noisy speech, are unexploited. In this paper, we adopt contrastive regularization (CR) built upon contrastive learnin…
This study investigates the Lombard effect, where individuals adapt their speech in noisy environments. We introduce an enhanced Mandarin Lombard grid (EMALG) corpus with meaningful sentences, enhancing the Mandarin Lombard grid (MALG) corpus. EMALG features 34 speakers and improves recording setups…
Deep learning-based approaches formulate acoustic echo cancellation (AEC) as a supervised speech separation task, where the mixture signal and the far-end signal are combined directly before or after the encoding stage. However, the mixture signal and the far-end signal are not integrated sufficient…
End-to-end neural speech coding achieves state-of-the-art performance by using residual vector quantization. However, it is a challenge to quantize the latent variables with as few bits as possible. In this paper, we propose SRCodec, a neural speech codec that relies on a fully convolutional encoder…
Neural speech coding is a rapidly developing topic, where state-of-the-art approaches now exhibit superior compression performance than conventional methods. Despite significant progress, existing methods still have limitations in preserving and reconstructing fine details for optimal reconstruction…
Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either extract dirty content information with speaker information leaked in, or demand a…
We propose MiT-Net, a novel mix-transformer neural network with a pyramid encoder operating in the time domain, for the task of acoustic echo cancellation. The MiT-Net formulates acoustic echo cancellation as a supervised speech separation problem, in which near-end speech is separated from a single…
Existing automatic sleep staging algorithms rely on accurately labeled data. However, due to the subjectivity of sleep experts, accurate labels must be obtained through joint labeling by multiple experts, which results in high time and labor costs. In this work, we treat labels mislabeled by a singl…
This paper presents a Paired Mandarin Matrix Sentence Dataset (PMMSD), which will be available after publication. PMMSD is the first Mandarin matrix sentence intelligibility dataset containing both plain and Lombard speech for scientific research. The results verify that different Lombard styles wou…
Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, a natural speech contains many fast-changing and relatively briefly acoustic events, therefore, capturing the most informative speech features…
Most of current best performing Acoustic Scene Classification (ASC) systems utilize Mel scale spectrograms with Convolutional Neural Networks (CNNs). Mel scale is a common way to suit frequency warping of human ears, with strict decreasing frequency resolution on low to high frequency range. However…
NHK has proposed a famous 3D audio system: 22.2 multi-channel system, but its loudspeakers are too many and are troublesome to put in home. Ando and Wang has proposed two simplification methods to reduce its channel number, but only 3D sound field at the central listening point can be recovered well…
This paper proposes a general multichannel system reproduction method. Firstly, relative to original multichannel system, a general global model is build up by guaranteeing sound pressure and the direction of particle velocity at the receiving point constant, and making the square error of particle…