Despite progress in audio classification, a generalization gap remains between speech and other sound domains, such as environmental sounds and music. Models trained for speech tasks often fail to perform well on environmental or musical audio tasks, and vice versa. While self-supervised (SSL) audio representations offer an alternative, there has been limited exploration of scaling both model and dataset sizes for SSL-based general audio classification. We introduce Dasheng, a simple SSL audio encoder, based on the efficient masked autoencoder framework. Trained with 1.2 billion parameters on 272,356 hours of diverse audio, Dasheng obtains significant performance gains on the HEAR benchmark. It outperforms previous works on CREMA-D, LibriCount, Speech Commands, VoxLingua, and competes well in music and environment classification. Dasheng features inherently contain rich speech, music, and environmental information, as shown in nearest-neighbor classification experiments. Code is available https://github.com/richermans/dasheng/.
MAT-SED: A Masked Audio Transformer with Masked-Reconstruction Based Pre-training for Sound Event Detection
National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China（中国科学技术大学国家语音语言信息处理工程研究中心）；ICT Cluster, Singapore Institute of Technology（新加坡理工学院ICT集群）；The Australian National University（澳大利亚国立大学）
The lack of annotated training data in bioacoustics hinders the use of large-scale neural network models trained in a supervised way. In order to leverage a large amount of unannotated audio data, we propose AVES (Animal Vocalization Encoder based on Self-Supervision), a self-supervised, transformer-based audio representation model for encoding animal vocalizations. We pretrain AVES on a diverse set of unannotated audio datasets and fine-tune them for downstream bioacoustics tasks. Comprehensive experiments with a suite of classification and detection tasks have shown that AVES outperforms all…
Institute for Signal Processing, University of Luebeck（吕贝克大学信号处理研究所）；Graduate School for Computing in Medicine and Life Sciences, University of Luebeck（吕贝克大学医学与生命科学计算研究生院）
We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classification for domestic audio tagging in the DCASE-2016 contest. Our network achieved an average accuracy of 84.5% on the four-fold cross-validation for acoustic scene recognition, compared to the provided baseline of 72.5%, and an average equal error rate of 0.17 for domestic audio tagging, compared to the baseline of 0.21. The network therefore improves the baselines by a relative amount of 17% and 19%, respectively. The ne…
Exploring bat song syllable representations in self-supervised audio encoders
Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science（莱布尼茨进化与生物多样性科学自然博物馆）
Transformer-based models attain excellent results and generalize well when trained on sufficient amounts of data. However, constrained by the limited data available in the audio domain, most transformer-based models for audio tasks are finetuned from pre-trained models in other domains (e.g. image), which has a notable gap with the audio domain. Other methods explore the self-supervised learning approaches directly in the audio domain but currently do not perform well in the downstream tasks. In this paper, we present a novel self-supervised learning method for transformer-based audio models, …
NTT Communication Science Laboratories（NTT通信科学实验室）；NTT Corporation（NTT公司）
Recent general-purpose audio representations show state-of-the-art performance on various audio tasks. These representations are pre-trained by self-supervised learning methods that create training signals from the input. For example, typical audio contrastive learning uses temporal relationships among input sounds to create training signals, whereas some methods use a difference among input views created by data augmentations. However, these training signals do not provide information derived from the intact input sound, which we think is suboptimal for learning representation that describes …
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；UT Austin（德克萨斯大学奥斯汀分校）；IBM Research AI（IBM研究院人工智能部门）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Goethe University Frankfurt（法兰克福大学）
We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used directly in typical bidirectional speech recognition models. The pre-trained networks can then be fine-tuned on a smaller amount of supervised data for speech recognition. Experiments with this approach on the LibriSpeech and Wall Street Journal corpora show promising results. We find that the main factors that lead to speech recognition improvements are: masking segments of sufficient width in both time and frequency, pre…
This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder, as audio spectrograms are highly correlated in lo…
An overview of neural architectures for self-supervised audio representation learning from masked spectrograms
Department of Electronic Systems, Aalborg University（电子系统系，奥尔堡大学）；Pioneer Centre for Artificial Intelligence（人工智能先锋中心）；National and Kapodistrian University of Athens（雅典国家与卡波蒂斯坦大学）
The Interspeech 2026 Audio Encoder Capability Challenge for Large Audio Language Models
MiLM Plus, Xiaomi Inc., Beijing, China（小米公司，北京）；DataoceanAI Inc., USA（DataoceanAI公司，美国）；Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK（视觉语音与信号处理中心， Surrey大学， Guildford，英国）；Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（深圳国际研究生院，清华大学，深圳，中国）
Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；Meta AI
Video recordings of speech contain correlated audio and visual information, providing a strong signal for speech representation learning from the speaker's lip movements and the produced sound. We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech, which masks multi-stream video input and predicts automatically discovered and iteratively refined multimodal hidden units. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition. On the largest public lip-…
Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan（国立台湾大学通信工程研究所）；Amazon AGI, Bellevue, United States（亚马逊人工智能研究院）
In this paper, we propose a simple yet powerful improvement over the recent Self-Supervised Audio Spectrogram Transformer (SSAST) model for speech and audio classification. Specifically, we leverage the insight that the SSAST uses a very high masking ratio (75%) during pretraining, meaning that the vast majority of self-attention compute is performed on mask tokens. We address this by integrating the encoder-decoder architecture from Masked Autoencoders are Scalable Vision Learners (MAE) into the SSAST, where a deep encoder operates on only unmasked input, and a shallow decoder operates on enc…
Department of Electronic Engineering University of York（约克大学电子工程系）；University of York（约克大学）
We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the features and allows an automated exploration of the features combination analogous to hand-crafting the most relevant features for the recognition task. MCLNN has achieved competitive recognition accuracies on the GTZAN and the ISMIR2004 music datasets that surpass several state-of-the-…
Wake Forest University（维克森林大学）；University of Montreal（蒙特利尔大学）
Automated audio captioning aims to use natural language to describe the content of audio data. This paper presents an audio captioning system with an encoder-decoder architecture, where the decoder predicts words based on audio features extracted by the encoder. To improve the proposed system, transfer learning from either an upstream audio-related task or a large in-domain dataset is introduced to mitigate the problem induced by data scarcity. Besides, evaluation metrics are incorporated into the optimization of the model with reinforcement learning, which helps address the problem of ``expos…
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Huawei Translation Services Center（华为翻译服务中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；NiuTrans Research（NiuTrans研究院）
School of Computing and Information Systems, The University of Melbourne, Australia（墨尔本大学计算机与信息系统学院）；Baskin School of Engineering, University of California, Santa Cruz, USA（加州大学圣克鲁兹分校工程学院）；Institute of Trustworthy Embodied AI, Fudan University, China（复旦大学可信具身人工智能研究所）
