Contrastive Audio-Language Learning for Music

• URL: http://arxiv.org/abs/2208.12208v1
• Date: Thu, 25 Aug 2022 16:55:15 GMT
• Title: Contrastive Audio-Language Learning for Music
• Authors: Ilaria Manco, Emmanouil Benetos, Elio Quinton, Gy\"orgy Fazekas
• Abstract summary: MusCALL is a framework for Music Contrastive Audio-Language Learning. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences.
• Score: 13.699088044513562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As one of the most intuitive interfaces known to humans, natural language has the potential to mediate many tasks that involve human-computer interaction, especially in application-focused fields like Music Information Retrieval. In this work, we explore cross-modal learning in an attempt to bridge audio and language in the music domain. To this end, we propose MusCALL, a framework for Music Contrastive Audio-Language Learning. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences, producing multimodal embeddings that can be used for text-to-audio and audio-to-text retrieval out-of-the-box. Thanks to this property, MusCALL can be transferred to virtually any task that can be cast as text-based retrieval. Our experiments show that our method performs significantly better than the baselines at retrieving audio that matches a textual description and, conversely, text that matches an audio query. We also demonstrate that the multimodal alignment capability of our model can be successfully extended to the zero-shot transfer scenario for genre classification and auto-tagging on two public datasets.

Related papers

• MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models [11.834712543531756]
  MuChoMusic is a benchmark for evaluating music understanding in multimodal language models focused on audio. It comprises 1,187 multiple-choice questions, all validated by human annotators, on 644 music tracks sourced from two publicly available music datasets. We evaluate five open-source models and identify several pitfalls, including an over-reliance on the language modality.
  arXiv  Detail & Related papers  (2024-08-02T15:34:05Z)
• Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models [98.34889301515412]
  We develop the Qwen-Audio model and address the limitation by scaling up audio-language pre-training to cover over 30 tasks and various audio types. Qwen-Audio achieves impressive performance across diverse benchmark tasks without requiring any task-specific fine-tuning. We further develop Qwen-Audio-Chat, which allows for input from various audios and text inputs, enabling multi-turn dialogues and supporting various audio-central scenarios.
  arXiv  Detail & Related papers  (2023-11-14T05:34:50Z)
• AudioPaLM: A Large Language Model That Can Speak and Listen [79.44757696533709]
  We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models. It can process and generate text and speech with applications including speech recognition and speech-to-speech translation.
  arXiv  Detail & Related papers  (2023-06-22T14:37:54Z)
• Exploring the Role of Audio in Video Captioning [59.679122191706426]
  We present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. We propose new local-global fusion mechanisms to improve information exchange across audio and video.
  arXiv  Detail & Related papers  (2023-06-21T20:54:52Z)
• Language-Guided Music Recommendation for Video via Prompt Analogies [35.48998901411509]
  We propose a method to recommend music for an input video while allowing a user to guide music selection with free-form natural language. Existing music video datasets provide the needed (video, music) training pairs, but lack text descriptions of the music.
  arXiv  Detail & Related papers  (2023-06-15T17:58:01Z)
• AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head [82.69233563811487]
  Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. We propose a multi-modal AI system named AudioGPT, which complements LLMs with foundation models to process complex audio information.
  arXiv  Detail & Related papers  (2023-04-25T17:05:38Z)
• Language-Guided Audio-Visual Source Separation via Trimodal Consistency [64.0580750128049]
  A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform. We adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets.
  arXiv  Detail & Related papers  (2023-03-28T22:45:40Z)
• MuLan: A Joint Embedding of Music Audio and Natural Language [15.753767984842014]
  This paper presents a new generation of models that link audio annotations directly to natural language descriptions. MuLan takes the form of a two-tower, joint audio-text embedding model trained using 44 million music recordings.
  arXiv  Detail & Related papers  (2022-08-26T03:13:21Z)
• Learning music audio representations via weak language supervision [14.335950077921435]
  We design a multimodal architecture for music and language pre-training (MuLaP) optimised via a set of proxy tasks. weak supervision is provided in the form of noisy natural language descriptions conveying the overall musical content of the track. We demonstrate the usefulness of our approach by comparing the performance of audio representations produced by the same audio backbone with different training strategies.
  arXiv  Detail & Related papers  (2021-12-08T10:30:52Z)
• MusCaps: Generating Captions for Music Audio [14.335950077921435]
  We present the first music audio captioning model, MusCaps, consisting of an encoder-decoder with temporal attention. Our method combines convolutional and recurrent neural network architectures to jointly process audio-text inputs. Our model represents a shift away from classification-based music description and combines tasks requiring both auditory and linguistic understanding.
  arXiv  Detail & Related papers  (2021-04-24T16:34:47Z)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.