Late multimodal fusion for image and audio music transcription

• URL: http://arxiv.org/abs/2204.03063v1
• Date: Wed, 6 Apr 2022 20:00:33 GMT
• Title: Late multimodal fusion for image and audio music transcription
• Authors: Mar\'ia Alfaro-Contreras (1), Jose J. Valero-Mas (1), Jos\'e M. I\~nesta (1) and Jorge Calvo-Zaragoza (1) ((1) Instituto Universitario de Investigaci\'on Inform\'atica, University of Alicante, Alicante, Spain)
• Abstract summary: multimodal image and audio music transcription comprises the challenge of effectively combining the information conveyed by image and audio modalities. We study four combination approaches in order to merge, for the first time, the hypotheses regarding end-to-end OMR and AMT systems. Two of the four strategies considered significantly improve the corresponding unimodal standard recognition frameworks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Music transcription, which deals with the conversion of music sources into a structured digital format, is a key problem for Music Information Retrieval (MIR). When addressing this challenge in computational terms, the MIR community follows two lines of research: music documents, which is the case of Optical Music Recognition (OMR), or audio recordings, which is the case of Automatic Music Transcription (AMT). The different nature of the aforementioned input data has conditioned these fields to develop modality-specific frameworks. However, their recent definition in terms of sequence labeling tasks leads to a common output representation, which enables research on a combined paradigm. In this respect, multimodal image and audio music transcription comprises the challenge of effectively combining the information conveyed by image and audio modalities. In this work, we explore this question at a late-fusion level: we study four combination approaches in order to merge, for the first time, the hypotheses regarding end-to-end OMR and AMT systems in a lattice-based search space. The results obtained for a series of performance scenarios -- in which the corresponding single-modality models yield different error rates -- showed interesting benefits of these approaches. In addition, two of the four strategies considered significantly improve the corresponding unimodal standard recognition frameworks.

Related papers

• End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music [12.779526750915707]
  We present the first truly end-to-end approach for page-level Optical Music Recognition. Our system processes an entire music score page and outputs a complete transcription in a music encoding format. The results demonstrate that our system not only successfully transcribes full-page music scores but also outperforms the commercial tool in both zero-shot settings and after fine-tuning with the target domain.
  arXiv  Detail & Related papers  (2024-05-20T15:21:48Z)
• Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription [13.960714900433269]
  Sheet Music Transformer is the first end-to-end OMR model designed to transcribe complex musical scores without relying solely on monophonic strategies. Our model has been tested on two polyphonic music datasets and has proven capable of handling these intricate music structures effectively.
  arXiv  Detail & Related papers  (2024-02-12T11:52:21Z)
• Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing [72.56219471145232]
  We propose a ST/MT multi-tasking framework with hard parameter sharing. Our method reduces the speech-text modality gap via a pre-processing stage. We show that our framework improves attentional encoder-decoder, Connectionist Temporal Classification (CTC), transducer, and joint CTC/attention models by an average of +0.5 BLEU.
  arXiv  Detail & Related papers  (2023-09-27T17:48:14Z)
• Towards Robust and Truly Large-Scale Audio-Sheet Music Retrieval [4.722882736419499]
  Cross-modal deep learning is used to learn joint embedding spaces that link the two distinct modalities - audio and sheet music images. While there has been steady improvement on this front over the past years, a number of open problems still prevent large-scale employment of this methodology. We identify a set of main challenges on the road towards robust and large-scale cross-modal music retrieval in real scenarios.
  arXiv  Detail & Related papers  (2023-09-21T15:11:16Z)
• Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [109.1912721224697]
  We present the Unified Frequency-Assisted transFormer framework, named UFAFormer, to address the DGM4 problem. By leveraging the discrete wavelet transform, we decompose images into several frequency sub-bands, capturing rich face forgery artifacts. Our proposed frequency encoder, incorporating intra-band and inter-band self-attentions, explicitly aggregates forgery features within and across diverse sub-bands.
  arXiv  Detail & Related papers  (2023-09-18T11:06:42Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• Align, Adapt and Inject: Sound-guided Unified Image Generation [50.34667929051005]
  We propose a unified framework 'Align, Adapt, and Inject' (AAI) for sound-guided image generation, editing, and stylization. Our method adapts input sound into a sound token, like an ordinary word, which can plug and play with existing Text-to-Image (T2I) models. Our proposed AAI outperforms other text and sound-guided state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-20T12:50:49Z)
• Zorro: the masked multimodal transformer [68.99684436029884]
  Zorro is a technique that uses masks to control how inputs from each modality are routed inside Transformers. We show that with contrastive pre-training Zorro achieves state-of-the-art results on most relevant benchmarks for multimodal tasks.
  arXiv  Detail & Related papers  (2023-01-23T17:51:39Z)
• An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition [24.377724078096144]
  Piano and orchestral scores frequently exhibit polyphonic passages, which add a second dimension to the task. We propose two novel formulations for end-to-end polyphonic OMR. We observe a new state-of-the-art performance with our multi-sequence detection decoder, RNNDecoder.
  arXiv  Detail & Related papers  (2021-08-03T22:04:40Z)
• A framework to compare music generative models using automatic evaluation metrics extended to rhythm [69.2737664640826]
  This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music. The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces using automatic quantitative metrics based on geometry which have rhythm support added as well.
  arXiv  Detail & Related papers  (2021-01-19T15:04:46Z)
• Optical Music Recognition: State of the Art and Major Challenges [0.0]
  Optical Music Recognition (OMR) is concerned with transcribing sheet music into a machine-readable format. The transcribed copy should allow musicians to compose, play and edit music by taking a picture of a music sheet. Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach.
  arXiv  Detail & Related papers  (2020-06-14T12:40:17Z)

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.