Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings

• URL: http://arxiv.org/abs/2210.00434v2
• Date: Mon, 8 May 2023 03:09:27 GMT
• Title: Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings
• Authors: Zhihuan Kuang, Shi Zong, Jianbing Zhang, Jiajun Chen, Hongfu Liu
• Abstract summary: Music-to-text synaesthesia aims to generate descriptive texts from music recordings with the same sentiment for further understanding. We build a computational model to generate sentences that can describe the content of the music recording. To tackle the highly non-discriminative classical music, we design a group topology-preservation loss.
• Score: 36.090928638883454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we consider a novel research problem: music-to-text synaesthesia. Different from the classical music tagging problem that classifies a music recording into pre-defined categories, music-to-text synaesthesia aims to generate descriptive texts from music recordings with the same sentiment for further understanding. As existing music-related datasets do not contain the semantic descriptions on music recordings, we collect a new dataset that contains 1,955 aligned pairs of classical music recordings and text descriptions. Based on this, we build a computational model to generate sentences that can describe the content of the music recording. To tackle the highly non-discriminative classical music, we design a group topology-preservation loss, which considers more samples as a group reference and preserves the relative topology among different samples. Extensive experimental results qualitatively and quantitatively demonstrate the effectiveness of our proposed model over five heuristics or pre-trained competitive methods and their variants on our collected dataset.

Related papers

• Optical Music Recognition in Manuscripts from the Ricordi Archive [6.274767633959002]
  Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. We have automatically extracted samples that represent various musical elements depicted on the manuscripts, including notes, staves, clefs, erasures, and composer's annotations. We trained multiple neural network-based classifiers to differentiate between the identified music elements.
  arXiv  Detail & Related papers  (2024-08-14T09:29:11Z)
• MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
  We are inspired by how musicians compose music not just from a movie script, but also through visualizations. We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
  arXiv  Detail & Related papers  (2024-06-07T06:38:59Z)
• ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models [67.66825818489406]
  This paper introduces a text-to-waveform music generation model, underpinned by the utilization of diffusion models. Our methodology hinges on the innovative incorporation of free-form textual prompts as conditional factors to guide the waveform generation process. We demonstrate that our generated music in the waveform domain outperforms previous works by a large margin in terms of diversity, quality, and text-music relevance.
  arXiv  Detail & Related papers  (2023-02-09T06:27:09Z)
• Noise2Music: Text-conditioned Music Generation with Diffusion Models [73.74580231353684]
  We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models.
  arXiv  Detail & Related papers  (2023-02-08T07:27:27Z)
• A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
  This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
  arXiv  Detail & Related papers  (2022-11-21T14:15:43Z)
• Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation [18.2750732408488]
  We exploit the crowd-sourced music comments to construct a new dataset and propose a sequence-to-sequence model to generate text descriptions of music. To enhance the authenticity and thematicity of generated texts, we propose a discriminator and a novel topic evaluator.
  arXiv  Detail & Related papers  (2022-09-05T14:51:51Z)
• 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)
• Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music [73.73045854068384]
  We propose to transcribe the lyrics of polyphonic music using a novel genre-conditioned network. The proposed network adopts pre-trained model parameters, and incorporates the genre adapters between layers to capture different genre peculiarities for lyrics-genre pairs. Our experiments show that the proposed genre-conditioned network outperforms the existing lyrics transcription systems.
  arXiv  Detail & Related papers  (2022-04-07T09:15:46Z)

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.