Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning
We present a novel multimodal multitask network and associated training algorithm. The method is capable of ingesting data from approximately 12 different modalities namely image video audio text depth point cloud ti…
3D Point Cloud ClassificationAction ClassificationAction RecognitionAudio Classification+6On the choice of the optimal temporal support for audio classification with Pre-trained embeddings
Current state-of-the-art audio analysis systems rely on pre-trained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of tasks is the subject of many recent public…
Audio ClassificationStethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification
Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samp…
Audio ClassificationContrastive LearningDomain AdaptationLung Sound Classification+1Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Parameter-efficient transfer learning (PETL) methods have emerged as a solid alternative to the standard full fine-tuning approach. They only train a few extra parameters for each downstream task, without sacrificing per…
Audio ClassificationFew-Shot LearningTransfer LearningAcoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) have shown their promise in solving a wide …
Audio ClassificationFew-Shot Audio ClassificationLanguage ModelingLanguage Modelling+1Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach
Formally verifying audio classification systems is essential to ensure accurate signal classification across real-world applications like surveillance, automotive voice commands, and multimedia content management, preven…
Audio ClassificationClassificationManagementInvestigating the Emergent Audio Classification Ability of ASR Foundation Models
Text and vision foundation models can perform many tasks in a zero-shot setting, a desirable property that enables these systems to be applied in general and low-resource settings. There has been far less work, however, …
Audio ClassificationDecoderspeech-recognitionSpeech Recognition+3Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans. However, the absence of pre-trained audio models capable of handling diverse audio types and tasks ha…
Acoustic Scene ClassificationAudio captioningAudio ClassificationAutomatic Speech Recognition (ASR)+4Pruning random resistive memory for optimizing analogue AI
The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and…
Audio ClassificationImage SegmentationSemantic SegmentationAdversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
Deep generative models have emerged as a promising approach in the medical image domain to address data scarcity. However, their use for sequential data like respiratory sounds is less explored. In this work, we propose …
Audio ClassificationSound ClassificationMirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
One of the main challenges of multimodal learning is the need to combine heterogeneous modalities (e.g., video, audio, text). For example, video and audio are obtained at much higher rates than text and are roughly align…
Action ClassificationAudio ClassificationVideo Question AnsweringAuto deep learning for bioacoustic signals
This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against traditional manually-designed deep learning mo…
Audio ClassificationClassificationDeep LearningFeature Engineering+2OmniVec: Learning robust representations with cross modal sharing
Majority of research in learning based methods has been towards designing and training networks for specific tasks. However, many of the learning based tasks, across modalities, share commonalities and could be potential…
3D Point Cloud ClassificationAction ClassificationAudio ClassificationFine-Grained Image Classification+3Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models
The introduction of large-scale audio datasets, such as AudioSet, paved the way for Transformers to conquer the audio domain and replace CNNs as the state-of-the-art neural network architecture for many tasks. Audio Spec…
Audio ClassificationAudio TaggingInstrument RecognitionKnowledge DistillationCLARA: Multilingual Contrastive Learning for Audio Representation Acquisition
Multilingual speech processing requires understanding emotions, a task made difficult by limited labelled data. CLARA, minimizes reliance on labelled data, enhancing generalization across languages. It excels at fosterin…
Audio ClassificationContrastive LearningCross-Lingual TransferData Augmentation+6Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
As the number of parameters in Deep Neural Networks (DNNs) scales, the thirst for training data also increases. To save costs, it has become common for users and enterprises to delegate time-consuming data collection to …
Audio Classificationimage-classificationImage Classificationtext-classification+1CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representation between audio and language modalities …
AttributeAudio ClassificationZero-shot Audio ClassificationDiffusion Models as Masked Audio-Video Learners
Over the past several years, the synchronization between audio and visual signals has been leveraged to learn richer audio-visual representations. Aided by the large availability of unlabeled videos, many unsupervised tr…
Audio ClassificationContrastive LearningLanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
The video-language (VL) pretraining has achieved remarkable improvement in multiple downstream tasks. However, the current VL pretraining framework is hard to extend to multiple modalities (N modalities, N>=3) beyond vis…
Audio ClassificationContrastive LearningMultimodal Deep LearningScene Classification (unified classes)+11Audio classification with Dilated Convolution with Learnable Spacings
Dilated convolution with learnable spacings (DCLS) is a recent convolution method in which the positions of the kernel elements are learned throughout training by backpropagation. Its interest has recently been demonstra…
Audio ClassificationAudio TaggingClassification