English

Introducing Auxiliary Text Query-modifier to Content-based Audio Retrieval

Audio and Speech Processing 2022-07-21 v1 Computation and Language Information Retrieval Machine Learning Sound

Abstract

The amount of audio data available on public websites is growing rapidly, and an efficient mechanism for accessing the desired data is necessary. We propose a content-based audio retrieval method that can retrieve a target audio that is similar to but slightly different from the query audio by introducing auxiliary textual information which describes the difference between the query and target audio. While the range of conventional content-based audio retrieval is limited to audio that is similar to the query audio, the proposed method can adjust the retrieval range by adding an embedding of the auxiliary text query-modifier to the embedding of the query sample audio in a shared latent space. To evaluate our method, we built a dataset comprising two different audio clips and the text that describes the difference. The experimental results show that the proposed method retrieves the paired audio more accurately than the baseline. We also confirmed based on visualization that the proposed method obtains the shared latent space in which the audio difference and the corresponding text are represented as similar embedding vectors.

Keywords

Cite

@article{arxiv.2207.09732,
  title  = {Introducing Auxiliary Text Query-modifier to Content-based Audio Retrieval},
  author = {Daiki Takeuchi and Yasunori Ohishi and Daisuke Niizumi and Noboru Harada and Kunio Kashino},
  journal= {arXiv preprint arXiv:2207.09732},
  year   = {2022}
}

Comments

Accepted to Interspeech 2022

R2 v1 2026-06-25T01:04:26.272Z