Pith. sign in

REVIEW 3 cited by

A Survey on Audio Synthesis and Audio-Visual Multimodal Processing

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2108.00443 v1 pith:NA3YIVAM submitted 2021-08-01 eess.AS cs.SD

classification eess.AScs.SD
keywords multimodalaudioaudio-visualprocessingsynthesissurveydevelopmentfuture
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

With the development of deep learning and artificial intelligence, audio synthesis has a pivotal role in the area of machine learning and shows strong applicability in the industry. Meanwhile, significant efforts have been dedicated by researchers to handle multimodal tasks at present such as audio-visual multimodal processing. In this paper, we conduct a survey on audio synthesis and audio-visual multimodal processing, which helps understand current research and future trends. This review focuses on text to speech(TTS), music generation and some tasks that combine visual and acoustic information. The corresponding technical methods are comprehensively classified and introduced, and their future development trends are prospected. This survey can provide some guidance for researchers who are interested in the areas like audio synthesis and audio-visual multimodal processing.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Testing chatbots on the creation of encoders for audio conditioned image generation

    cs.SD 2025-09 conditional novelty 6.0 of 10

    All chatbot-designed audio encoders failed to align with CLIP text embeddings and produced incoherent images, while showing a surprising architectural similarity across chatbots.

  2. Learning from Silence and Noise for Visual Sound Source Localization

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Adding silence and Gaussian noise as negative training pairs improves self-supervised visual sound source localization, and the authors provide IS3+ and a separability metric.

  3. Effectively obtaining acoustic, visual and textual data from videos

    cs.MM 2025-09 conditional novelty 4.0 of 10

    A video-processing pipeline created a 2.24 million-sample audio-image-text dataset, with text captions generated by BLIP from video frames.

Pith tools