21 accepted papers
The query-based universal sound separation (QUSS) has been addressed, aiming to perform the separation of specific sound sources based on a given query. Most of existed methods focus on the improvement of separation models, ignoring the influence of category-conditioned query embedding distribution…
Language Model (LM)-based Text-to-Speech (TTS) systems often generate hallucinated speech that deviates from input text. Existing mitigation strategies either demand excessive training resources or introduce significant inference latency. In this paper, we propose GFlOwNet-guided distribution Alignm
Overlapping sound events are ubiquitous in real-world environments, but existing end-to-end sound event detection (SED) methods still struggle to detect them effectively. A critical reason is that these methods represent overlapping events using shared and entangled frame-wise features, which degrad…
Despite the success of self-supervised respiratory sound classification methods, they do not consider that respiratory sounds are quasi-periodic signals with repetitive patterns in successive breaths, which is vital for distinguishing respiratory sounds from non-quasi-periodic sounds like noises. Th…
A great deal of recent research reveals that artifacts introduced by spoofing algorithms reside in specific frequency subbands or temporal segments. Therefore, the performance of spoofing detection can be improved by focusing on these regions. However, it is difficult for the detection system to cho…
Multimodal Sentiment Analysis (MSA) has made great progress that benefits from extraordinary fusion scheme. However, there is a lack of labeled data, resulting in severe overfitting and poor generalization for supervised models applied in this field. In this paper, we propose Sentiment Knowledge Enh…
In the domain of sound event detection (SED), Convolutional Recurrent Neural Network (CRNN) has become the most successful architecture, which adopts Recurrent Neural Network (RNN) to model temporal dependencies from the output of Convolutional Neural Network (CNN). However, CRNN does not fully use…
Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency repres…
The lack of data and the difficulty of multimodal fusion have always been challenges for multimodal emotion recognition (MER). In this paper, we propose to use pre-trained models as upstream network, wav2vec 2.0 for audio modality and BERT for text modality, and finetune them in downstream task of M…
To solve the domain shift problem in speaker verification, one effective domain adaptation approach is to learn domain-invariant embeddings via aligning the source and target distributions in the embedding space. However, this approach could be problematic when the source and target domains are from…
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another pow…
Self-attention mechanism has been widely employed in semi-supervised sound event detection (SS-SED). In self-attention, since dependencies between pairwise features at all moments are captured, the irrelevant features of different classes of sounds and background sounds at other moments are inevitab…
This paper focuses on the problem of temporal dependency modeling in the CNN-based models for audio classification tasks. To capture audio temporal dependencies using CNNs, we take a different approach from the purely architecture-induced method and explicitly encode temporal dependencies into the C…
Respiratory sound classification refers to identifying adventitious sounds from given recordings automatically. Due to the difficulty of collection and the expensive manual annotation, there are only limited samples available, which impacts on learning better models. Meanwhile, a majority of these m…
Current methods for monaural speech enhancement only utilize acoustic information but seldom consider the phonetic information of an utterance. In the voice conversion community, significant progress has been achieved by using the phonetic information via the phonetic posteriorgrams (PPGs). Inspired…
The Softmax normalization function-based attention mechanism is often employed by End-to-End Automatic Speech Recognition (E2E ASR) models to tell the network where to focus within the input. However, this mechanism leads to the attention distribution becoming increasingly flatter as the input seque…
In this paper, a task-driven multilevel framework (TDMF) is proposed for end-to-end speaker verification. The TDMF has four layers, and each layer has different effects on speaker models or representations to implement the functions of universal background model (UBM), Gaussian mixture model (GMM),…
Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency (TF) domain. Such an approach will result in limited perceptual score, s…
In the studies of i-vector based speaker verification, the discriminative training of probabilistic linear discriminative analysis (PLDA) model has been proven to be an effective way to improve performance. This paper focuses on using a deep neural network (DNN) to strengthen the original discrimina…
Human action recognition from realistic scenarios is extremely challenging due to large intra-class variation and complex background clutters. In this paper, by leveraging the strength of deep learning and vector of locally aggregated descriptors (VLAD), we propose a new methods for human action rec…
Human action recognition from still image has recently drawn increasing attention in human behavior analysis vision and also poses great challenges due to the huge inter ambiguity and intra variability. Vector of locally aggregated descriptors (VLAD) has achieved state-of-the-art performance in many…