Papers Zero-shot Audio Classification
“Zero-shot Audio Classification” 태그가 달린 논문 18편 · 필터 해제
TSPE: Task-Specific Prompt Ensemble for Improved Zero-Shot Audio Classification
Audio-language models (ALMs) excel in zero-shot audio classification, a task where models classify previously unseen audio clips at test time by leveraging descriptive natural language prompts. We introduce TSPE (Task-Sp…
Audio ClassificationClassificationDescriptiveZero-shot Audio ClassificationA sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followed by category names. In this work, we ex…
Audio ClassificationClassificationContrastive LearningPrompt Engineering+1ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds
Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categories specified with natural language promp…
Audio ClassificationDescriptiveText RetrievalZero-shot Audio ClassificationMulti-label Zero-Shot Audio Classification with Temporal Attention
Zero-shot learning models are capable of classifying new classes by transferring knowledge from the seen classes using auxiliary information. While most of the existing zero-shot learning methods focused on single-label …
Audio ClassificationClassificationZero-shot Audio Classificationzero-shot-classification+1Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs
Research on multi-modal contrastive learning strategies for audio and text has rapidly gained interest. Contrastively trained Audio-Language Models (ALMs), such as CLAP, which establish a unified representation across au…
Audio ClassificationContrastive LearningRetrievalZero-shot Audio ClassificationInvestigating 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+3CompA: 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 ClassificationLanguageBind: 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)+11Exploring Meta Information for Audio-based Zero-shot Bird Classification
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is still an issue for rare and underrepresente…
Audio ClassificationZero-shot Audio ClassificationZero-Shot LearningImageBind: One Embedding Space To Bind Them All
We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train su…
AllCross-Modal RetrievalMultimodal Deep LearningRetrieval+10WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
The advancement of audio-language (AL) multimodal learning tasks has been significant in recent years. However, researchers face challenges due to the costly and time-consuming collection process of existing audio-langua…
Audio captioningEvent DetectionLanguage ModellingLarge Language Model+4Zero-Shot Audio Classification using Image Embeddings
Supervised learning methods can solve the given problem in the presence of a large set of labeled data. However, the acquisition of a dataset covering all the target classes typically requires manual labeling which is ex…
Audio ClassificationClassificationZero-shot Audio ClassificationZero-Shot LearningConnecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Machines that can represent and describe environmental soundscapes have practical potential, e.g., for audio tagging and captioning. Prevailing learning paradigms of audio-text connections have been relying on parallel a…
Audio ClassificationAudio TaggingRetrievalTransfer Learning+1Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Machines that can represent and describe environmental soundscapes have practical potential, e.g., for audio tagging and captioning systems. Prevailing learning paradigms have been relying on parallel audio-text data, wh…
Audio ClassificationAudio TaggingRetrievalTransfer Learning+1Sound-Guided Semantic Image Manipulation
The recent success of the generative model shows that leveraging the multi-modal embedding space can manipulate an image using text information. However, manipulating an image with other sources rather than text, such as…
Audio Classificationimage-classificationImage ClassificationImage Manipulation+1Zero-Shot Audio Classification with Factored Linear and Nonlinear Acoustic-Semantic Projections
In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. Zero-shot learning in audio classification…
Audio ClassificationClassificationGeneral ClassificationZero-shot Audio Classification+1Zero-Shot Audio Classification via Semantic Embeddings
In this paper, we study zero-shot learning in audio classification via semantic embeddings extracted from textual labels and sentence descriptions of sound classes. Our goal is to obtain a classifier that is capable of r…
Audio ClassificationClassificationGeneral ClassificationSentence+3Zero-Shot Audio Classification Based on Class Label Embeddings
This paper proposes a zero-shot learning approach for audio classification based on the textual information about class labels without any audio samples from target classes. We propose an audio classification system buil…
Audio ClassificationClassificationGeneral ClassificationZero-shot Audio Classification+1