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Investigating the Effective Dynamic Information of Spectral Shapes for audio classification

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1114-1126页

作者： Chen, Liangwei Zhou, Xiren Chen, Qiuju Xiong, Fang Chen, Huanhuan Univ Sci & Technol China Sch Artificial Intelligence & Data Sci Hefei 230027 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China Cent South Univ Xiangya Hosp Natl Clin Res Ctr Geriatr Dis Dept Otolaryngol Head & Neck Surg Changsha 410078 Peoples R China

The spectral shape holds crucial information for audio classification (AC), encompassing the spectrum's envelope, details, and dynamic changes over time. Conventional methods utilize cepstral coefficients for spectral shape description but overlook its variation details. Deep-learning approaches capture some dynamics but demand substantial training or fine-tuning resources. The Learning in the Model Space (LMS) framework precisely captures the dynamic information of temporal data by utilizing model fitting, even when computational resources and data are limited. However, applying LMS to audio faces challenges: 1) The high sampling rate of audio hinders efficient data fitting and capturing of dynamic information. 2) The Dynamic Information of Partial Spectral Shapes (DIPSS) may enhance classification, as only specific spectral shapes are relevant for AC. This paper extends an AC framework called Effective Dynamic Information Capture (EDIC) to tackle the above issues. EDIC constructs Mel-Frequency Cepstral Coefficients (MFCC) sequences within different dimensional intervals as the fitted data, which not only reduces the number of sequence sampling points but can also describe the change of the spectral shape in different parts over time. EDIC enables us to implement a topology-based selection algorithm in the model space, selecting effective DIPSS for the current AC task. The performance on three tasks confirms the effectiveness of EDIC.

关键词： Mel frequency cepstral coefficient Data models Spectral shape Computational modeling Feature extraction Fitting Music classification algorithms Training Multiple signal classification Learning in the model space dynamic information of the spectral shape audio classification mel-frequency cepstral coefficients echo state network

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CAS-TJ: Channel attention shuffle and temporal jigsaw for audio classification

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APPLIED ACOUSTICS 2025年 233卷

作者： Kim, Yongmin Ko, Kyungdeuk Lee, Junyeop Ko, Hanseok Korea Univ Engn Bldg 419145 Anam Ro Seoul 02841 South Korea

audio classification, including speech emotion, has been a topic of extensive research and applies to various virtual assistants and intelligent systems. Previous methods relied on handcrafted features such as spectrograms, but these features often have limitations due to their manual nature. Recently, hybrid models that use both end-to-end learning from raw audio with CNNs and Transformers have been developed to address this issue. However, when raw audio features are compressed through convolutional neural networks (CNNs), numerous channels are created, leading to redundancy or irrelevant information, while Transformers also have their limitations. Therefore, we propose Channel Attention Shuffle and Temporal Jigsaw (CAS-TJ) to generate more effective features and improve robustness. CAS divides channels into groups, multiplies them by attention weights, aggregates, and shuffles them. This process allows information to be exchanged among various channels, creating more discriminative channels. TJ generates frame patches of a specific size and uses mixing and matching during the learning process. This helps to better understand temporal relationships and detect discriminative patterns. Finally, we conduct experiments on the ESC-50 and Urban-8k datasets and find that the overall performance of CAS-TJ is better than the baseline models.

关键词： audio classification Channel attention and shuffle Temporal jigsaw

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Complementary Learning System Theory-based Active Learning for audio classification

Complementary Learning System Theory-based Active Learning f...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Geng, Hui Gao, Zijian Wan, Tianjiao Feng, Dawei Wang, Changjian Xu, Kele College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China

ISBN: (纸本)9798350368741

Deep learning has significantly advanced the audio classification, achieving remarkable results. However, these successes often rely on extensive manual annotation of audio, a labor-intensive and costly process. Active Learning (AL) presents a promising solution by minimizing the required amount of annotation through the iterative selection of the most informative audio samples. Current AL methods for audio classification typically depend solely on the latest model checkpoint, overlooking the dynamics of the entire training process. The Complementary Learning Systems (CLS) theory posits that the interplay between short-term and long-term memory systems can effectively measure sample uncertainty, offering a means to capture training dynamics. In this work, we introduce a novel AL framework for audio classification, termed CLS-AL, which addresses the limitations of existing methods by simultaneously maintaining both short-term and long-term memory models. This dual-memory approach allows for a more comprehensive consideration of training dynamics. The divergence in predictions between these memory models provides a new metric for evaluating the uncertainty of unlabeled samples, enhancing the effectiveness of the AL sample selection process. We demonstrate the effectiveness and generalizability of CLS-AL through extensive experiments on a diverse set of audio datasets, showing that CLS-AL obviously outperforms existing state-of-the-art methods. © 2025 IEEE.

关键词： Active Learning audio classification Complementary Learning System Uncertainty Estimation

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WhisPrompt: audio classification of Chinese opera genres by transferring time-series features

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Shi, Bin Wang, Hao Qiu, Jingwen Li, Xiaomin Nanjing Univ Sch Informat Management Nanjing 210023 Peoples R China Jiangsu Key Lab Data Engn & Knowledge Serv Nanjing 210023 Peoples R China

As one of the most famous intangible cultural heritages (ICHs) in China, the art of Chinese opera has a long history and profound cultural connotation, and is of great research value. The identification and archiving of opera data in audio modal is a key for digitization and knowledge mining on this precious ICH. In current studies, this work has been hampered by two "inconsistencies": between audio features and current classification models' representation, and between upstream and downstream tasks in pre-trained audio models. To address these problems, we propose WhisPrompt by transferring time-series features and apply WhisPrompt to Chinese Opera Genres dataset (COG). We contribute (1) a novel audio classification model WhisPrompt, guiding the pre-trained ASR model to achieve cross-task transfer learning via the Prompt paradigm, and experiments show that our model can better extract features from audio data, with a 4.50% improvement compared to the SOTA baseline;(2) an audio data mining perspective based on interpretability analysis, finding that WhisPrompt's focus when characterizing opera audio is consistent with human cognition, demonstrating the good interpretability and knowledge mining potential of our model. Together, these contributions drive research into future work in audio processing, digital humanities, and knowledge mining.

关键词： audio classification Digital humanities Transfer learning Prompt paradigm Whisper

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Beyond Spectrograms: Rethinking audio classification from EnCodec's Latent Space

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ALGORITHMS 2025年第2期18卷

作者： Perianez-Pascual, Jorge Gutierrez, Juan D. Escobar-Encinas, Laura Rubio-Largo, Alvaro Rodriguez-Echeverria, Roberto Univ Extremadura Inst Invest Tecnol Informat Aplicadas INTIA Ave Univ S-N Caceres 10003 Spain Univ Santiago De Compostela Dept Elect & Comp Sci Rua Benigno Ledo Lugo 27002 Spain Univ Extremadura Escuela Politecn Dept Comp & Telemat Syst Engn Caceres 10003 Spain

This paper presents a novel approach to audio classification leveraging the latent representation generated by Meta's EnCodec neural audio codec. We hypothesize that the compressed latent space representation captures essential audio features more suitable for classification tasks than the traditional spectrogram-based approaches. We train a vanilla convolutional neural network for music genre, speech/music, and environmental sound classification using EnCodec's encoder output as input to validate this. Then, we compare its performance training with the same network using a spectrogram-based representation as input. Our experiments demonstrate that this approach achieves comparable accuracy to state-of-the-art methods while exhibiting significantly faster convergence and reduced computational load during training. These findings suggest the potential of EnCodec's latent representation for efficient, faster, and less expensive audio classification applications. We analyze the characteristics of EnCodec's output and compare its performance against traditional spectrogram-based approaches, providing insights into this novel approach's advantages.

关键词： artificial intelligence audio classification deep learning foundation models

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audio classification using grasshopper-ride optimization algorithm-based support vector machine

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IET CIRCUITS DEVICES & SYSTEMS 2021年第5期15卷 434-447页

作者： Singh, Suryabhan Pratap Jaiswal, Umesh Chandra Madan Mohan Malaviya Univ Technol Dept Comp Sci & Engn Gorakhpur Uttar Pradesh India Madan Mohan Malaviya Univ Technol Dept Informat Technol & Comp Applicat Gorakhpur Uttar Pradesh India

The accurate and robust detection of the audio has been widely grown as the speech technology in the area of audio forensics, speech recognition, and so on. However, in real time, it is a challenge to deal with the massive data arriving from distributed sources. Thus, the study introduces a method that effectively deals with the data from the distributed sources using the map-reduce framework (MRF). The map and reduce function in MRF aim at feature extraction and audio classification. The robust classification using the proposed grasshopper-ride optimization algorithm-based support vector machine (G-ROA-based SVM) uses the features, such as multiple kernel Mel frequency cepstral coefficients, spectral flux, spectral kurtosis, and delta-amplitude modulation spectrogram. The proposed G-ROA is the integration of ROA and grasshopper optimization algorithm in tuning the optimal weights of SVM and also, the kernel function in SVM is modified using the Gaussian radial basis function, Gaussian kernel, and polynomial kernels. The experimentation of the proposed method is done using two datasets, namely TUT sound event 2017 dataset and ESC dataset. TUT sound event 2017 dataset consists of eight audio recordings from a single acoustic scene. ESC dataset consists of three parts and 252,400 recordings. The analysis reveals that the proposed audio classification acquired the maximal accuracy of 0.96, minimal false alarm rate, and false rejection rate of 0.022 and 0.0119, respectively.

关键词： audio signal processing feature extraction signal classification G-ROA-based SVM robust detection robust classification map-reduce framework accurate detection distributed sources speech recognition support vector machines radial basis function networks audio forensics audio recordings TUT sound event 2017 dataset grasshopper optimization algorithm audio recording audio classification grasshopper-ride optimization algorithm-based support vector machine multiple kernel Mel frequency cepstral coefficients cepstral analysis

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audio classification with Low-Rank Matrix Representation Features

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2013年第1期5卷 1–17页

作者： Shi, Ziqiang Han, Jiqing Zheng, Tieran Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Heilongjiang Peoples R China

In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the l(1)-norm is used to extract low-rank matrix features which are robust to white noise and gross corruption for audio signal. These low-rank matrix features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this linear classifier, most methods find the parameters, that is the weight matrix and bias in batch-mode, which makes it inefficient for large scale problems. In this article, we propose a parallel online framework using accelerated proximal gradient method. This framework has advantages in processing speed and memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of the general proximal gradient method was omitted, and this part of computing burden is saved in our approach. Extensive experiments on real data sets for laugh/non-laugh and applause/non-applause classification indicate that this novel framework is effective and noise robust.

关键词： Design Algorithms Performance audio classification low-rank matrix online learning trace norm minimization robust principle component analysis matrix classification proximal gradient

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audio classification using attention-augmented convolutional neural network

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KNOWLEDGE-BASED SYSTEMS 2018年 161卷 90-100页

作者： Wu, Yu Mao, Hua Yi, Zhang Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Sichuan Peoples R China

audio classification, as a set of important and challenging tasks, groups speech signals according to speakers' identities, accents, and emotional states. Due to the high dimensionality of the audio data, task-specific hand-crafted features extraction is always required and regarded cumbersome for various audio classification tasks. More importantly, the inherent relationship among features has not been fully exploited. In this paper, the original speech signal is first represented as spectrogram and later be split along the frequency domain to form frequency-distributed spectrogram. This paper proposes a task-independent model, called FreqCNN, to automaticly extract distinctive features from each frequency band by using convolutional kernels. Further more, an attention mechanism is introduced to systematically enhance the features from certain frequency bands. The proposed FreqCNN is evaluated on three publicly available speech databases thorough three independent classification tasks. The obtained results demonstrate superior performance over the state-of-the-art.

关键词： audio classification Spectrograms Convolutional neural networks Attention mechanism

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audio classification and categorization based on wavelets and support vector machine

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IEEE TRANSACTIONS ON SPEECH AND audio PROCESSING 2005年第5期13卷 644-651页

作者： Lin, CC Chen, SH Truong, TK Chang, Y I Shou Univ Dept Informat Engn Kaohsiung 840 Taiwan Shu Te Univ Dept Comp Sci & Informat Engn Kaohsiung 824 Taiwan

In this paper, an improved audio classification and categorization technique is presented. This technique makes use of wavelets and support vector machines (SVMs) to accurately classify and categorize audio data. When a query audio is given, wavelets are first applied to extract acoustical features such as subband power and pitch information. Then, the proposed method uses a bottom-up SVM over these acoustical features and additional parameters, such as frequency cepstral coefficients, to accomplish audio classification and categorization. A public audio database (Muscle Fish), which consists of 410 sounds in 16 classes, is used to evaluate the performances of the proposed method against other similar schemes. Experimental results show that the classification errors are reduced from 16 (8.1 %) to six (3.0 %), and the categorization accuracy of a given audio sound can achieve 100% in the Top 2 matches.

关键词： audio categorization audio classification support vector machine (SVM) wavelets

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audio classification using Extended Baum-Welch Transformations

Audio Classification using Extended Baum-Welch Transformatio...

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Interspeech Conference 2007

作者： Sainath, Tara N. Zue, Victor Kanevsky, Dimitri MIT Comp Sci & Artificial Intelligence Lab 32 Vassar St Cambridge MA 02139 USA IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA

ISBN: (纸本)9781605603162

audio classification has applications in a variety of contexts, such as automatic sound analysis, supervised audio segmentation and in audio information search and retrieval. Extended Baum-Welch (EBW) transformations are most commonly used as a discriminative technique for estimating parameters of Gaussian mixtures, though recently they have been applied in unsupervised audio segmentation. In this paper, we extend the use of these transformations to derive an audio classification algorithm. We find that our method outperforms both the Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) likelihood classification methods.\

关键词： audio classification gradient methods

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