Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
Self-supervised learning (SSL) has emerged as a popular approach for learning audio representations. One goal of audio self-supervised pre-training is to transfer knowledge to downstream audio tasks, generally including …
Audio ClassificationAudio TaggingEvent DetectionInstrument Recognition+6Few-shot Class-incremental Audio Classification Using Stochastic Classifier
It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only. When new classes emerge, the model needs to be retrained with adequate s…
Audio ClassificationClassificationFew-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes
Most existing methods for audio classification assume that the vocabulary of audio classes to be classified is fixed. When novel (unseen) audio classes appear, audio classification systems need to be retrained with abund…
Audio ClassificationClassificationBytes Are All You Need: Transformers Operating Directly On File Bytes
Modern deep learning approaches usually utilize modality-specific processing. For example, the most common deep learning approach to image classification involves decoding image file bytes into an RGB tensor which is pas…
AllAudio ClassificationClassificationimage-classification+3Audio classification using ML methods
Machine Learning systems have achieved outstanding performance in different domains. In this paper machine learning methods have been applied to classification task to classify music genre. The code shows how to extract …
Audio ClassificationClassificationAdapting Language-Audio Models as Few-Shot Audio Learners
We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data. Specifically, we designed CALM to retrieve the probabili…
Audio ClassificationClassificationFew-Shot Learningzero-shot-classification+1Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes.…
Audio ClassificationContrastive LearningSound ClassificationLEAN: Light and Efficient Audio Classification Network
Over the past few years, audio classification task on large-scale dataset such as AudioSet has been an important research area. Several deeper Convolution-based Neural networks have shown compelling performance notably V…
Audio ClassificationClassificationTransfer LearningONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
In this work, we explore a scalable way for building a general representation model toward unlimited modalities. We release ONE-PEACE, a highly extensible model with 4B parameters that can seamlessly align and integrate …
1 Image, 2*2 StitchiAction ClassificationAudioCapsAudio Classification+18Device-Robust Acoustic Scene Classification via Impulse Response Augmentation
The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphones introduce distributional shifts in the…
Acoustic Scene ClassificationAudio ClassificationClassificationDiversity+1Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of …
Audio ClassificationCompressing audio CNNs with graph centrality based filter pruning
Convolutional neural networks (CNNs) are commonplace in high-performing solutions to many real-world problems, such as audio classification. CNNs have many parameters and filters, with some having a larger impact on the …
Acoustic Scene ClassificationAudio ClassificationAudio TaggingScene ClassificationTransformer-based Sequence Labeling for Audio Classification based on MFCCs
Audio classification is vital in areas such as speech and music recognition. Feature extraction from the audio signal, such as Mel-Spectrograms and MFCCs, is a critical step in audio classification. These features are tr…
Audio ClassificationClassificationMultiscale Audio Spectrogram Transformer for Efficient Audio Classification
Audio event has a hierarchical architecture in both time and frequency and can be grouped together to construct more abstract semantic audio classes. In this work, we develop a multiscale audio spectrogram Transformer (M…
Audio ClassificationClassificationRepresentation LearningContent Adaptive Front End For Audio Classification
We propose a learnable content adaptive front end for audio signal processing. Before the modern advent of deep learning, we used fixed representation non-learnable front-ends like spectrogram or mel-spectrogram with/wit…
Audio ClassificationAudio Signal ProcessingClassificationScene UnderstandingUNFUSED: UNsupervised Finetuning Using SElf supervised Distillation
In this paper, we introduce UnFuSeD, a novel approach to leverage self-supervised learning and reduce the need for large amounts of labeled data for audio classification. Unlike prior works, which directly fine-tune a se…
Audio ClassificationSelf-Supervised LearningFace: Fast, Accurate and Context-Aware Audio Annotation and Classification
This paper presents a context-aware framework for feature selection and classification procedures to realize a fast and accurate audio event annotation and classification. The context-aware design starts with exploring f…
Active LearningAudio ClassificationClassificationEnvironmental Sound Classification+3HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification
Few-shot audio classification is an emerging topic that attracts more and more attention from the research community. Most existing work ignores the specificity of the form of the audio spectrogram and focuses largely on…
Audio ClassificationFew-Shot Audio ClassificationSpecificityFOSI: Hybrid First and Second Order Optimization
Popular machine learning approaches forgo second-order information due to the difficulty of computing curvature in high dimensions. We present FOSI, a novel meta-algorithm that improves the performance of any base first-…
Audio ClassificationLanguage ModellingSecond-order methodsTransfer LearningAdaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition
Spiking neural networks (SNN) are a promising research avenue for building accurate and efficient automatic speech recognition systems. Recent advances in audio-to-spike encoding and training algorithms enable SNN to be …
Audio ClassificationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition+1