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Found 28 papers, 14 papers with code
NM-FlowGAN: Modeling sRGB Noise with a Hybrid Approach based on Normalizing Flows and Generative Adversarial Networks
In our experiments, our NM-FlowGAN outperforms other baselines on the sRGB noise synthesis task.
HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods
Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems.
Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models
Diff-SV unifies a DPM-based speech enhancement system with a speaker embedding extractor, and yields a discriminative and noise-tolerable speaker representation through a hierarchical structure.
PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification
In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV systems to be less affected by noisy environments.
One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification
This paper suggests One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates KD and fine-tuning (FT).
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity
However, these methods rely on large-scale noisy-clean image pairs, which are difficult to obtain in practice.
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain.
Convolution channel separation and frequency sub-bands aggregation for music genre classification
In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative.
Extended U-Net for Speaker Verification in Noisy Environments
Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility.
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions.
RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies
Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems.
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains.
Ranked #1 on
Voice Anti-spoofing
on ASVspoof 2019 - LA
Attentive max feature map and joint training for acoustic scene classification
Furthermore, adopting the proposed attentive max feature map, our team placed fourth in the recent DCASE 2021 challenge.
Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System
By learning the reliable intermediate representation of the mean teacher network, we expect that the proposed method can explore more discriminatory embedding spaces and improve the generalization performance of the speaker verification system.
Graph Attention Networks for Speaker Verification
The proposed framework inputs segment-wise speaker embeddings from an enrollment and a test utterance and directly outputs a similarity score.
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
Single task deep neural networks that perform a target task among diverse cross-related tasks in the acoustic scene and event literature are being developed.
Capturing scattered discriminative information using a deep architecture in acoustic scene classification
Various experiments are conducted using the detection and classification of acoustic scenes and events 2020 task1-a dataset to validate the proposed methods.
Integrated Replay Spoofing-aware Text-independent Speaker Verification
In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach.
Segment Aggregation for short utterances speaker verification using raw waveforms
The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding.
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.
A study on the role of subsidiary information in replay attack spoofing detection
For addition, we utilize the multi-task learning framework to include subsidiary information to the code.
Self-supervised pre-training with acoustic configurations for replay spoofing detection
Constructing a dataset for replay spoofing detection requires a physical process of playing an utterance and re-recording it, presenting a challenge to the collection of large-scale datasets.
Cosine similarity-based adversarial process
In particular, the adversarial process degrades the performance of the subsidiary model by eliminating the subsidiary information in the input which, in assumption, may degrade the performance of the primary model.
Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge
To detect unrevealed characteristics that reside in a replayed speech, we directly input spectrograms into an end-to-end DNN without knowledge-based intervention.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification.
End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification
Each speaker basis is designed to represent the corresponding speaker in the process of training deep neural networks.
Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
The short duration of an input utterance is one of the most critical threats that degrade the performance of speaker verification systems.
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification.
