MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation
- URL: http://arxiv.org/abs/2501.03689v1
- Date: Tue, 07 Jan 2025 10:38:51 GMT
- Title: MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation
- Authors: Haojie Wei, Jun Yuan, Rui Zhang, Quanyu Dai, Yueguo Chen,
- Abstract summary: Music source separation and pitch estimation are vital tasks in music information retrieval.
We propose a Model-Agnostic Joint Learning framework for both tasks.
We show that MAJL outperforms state-of-the-art methods on both tasks.
- Score: 14.547438854536306
- License:
- Abstract: Music source separation and pitch estimation are two vital tasks in music information retrieval. Typically, the input of pitch estimation is obtained from the output of music source separation. Therefore, existing methods have tried to perform these two tasks simultaneously, so as to leverage the mutually beneficial relationship between both tasks. However, these methods still face two critical challenges that limit the improvement of both tasks: the lack of labeled data and joint learning optimization. To address these challenges, we propose a Model-Agnostic Joint Learning (MAJL) framework for both tasks. MAJL is a generic framework and can use variant models for each task. It includes a two-stage training method and a dynamic weighting method named Dynamic Weights on Hard Samples (DWHS), which addresses the lack of labeled data and joint learning optimization, respectively. Experimental results on public music datasets show that MAJL outperforms state-of-the-art methods on both tasks, with significant improvements of 0.92 in Signal-to-Distortion Ratio (SDR) for music source separation and 2.71% in Raw Pitch Accuracy (RPA) for pitch estimation. Furthermore, comprehensive studies not only validate the effectiveness of each component of MAJL, but also indicate the great generality of MAJL in adapting to different model architectures.
Related papers
- Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework [81.29965270493238]
We develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) for wireless communication applications.
The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard.
We introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data.
arXiv Detail & Related papers (2025-01-16T16:19:53Z) - Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging [33.23758947497205]
Advanced embedding models are typically developed using large-scale multi-task data and joint training across multiple tasks.
To overcome these challenges, we explore model merging-a technique that combines independently trained models to mitigate gradient conflicts and balance data distribution.
We introduce a novel method, Self Positioning, which efficiently searches for optimal model combinations within the space of task vectors using gradient descent.
arXiv Detail & Related papers (2024-10-19T08:39:21Z) - Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts [20.202031878825153]
We propose a novel dynamic data mixture for MoE instruction tuning.
Inspired by MoE's token routing preference, we build dataset-level representations and then capture the subtle differences among datasets.
Results on two MoE models demonstrate the effectiveness of our approach on both downstream knowledge & reasoning tasks and open-ended queries.
arXiv Detail & Related papers (2024-06-17T06:47:03Z) - Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance.
We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features.
In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
arXiv Detail & Related papers (2024-06-12T08:30:16Z) - Merging Multi-Task Models via Weight-Ensembling Mixture of Experts [64.94129594112557]
Merging Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently.
Previous methods, exemplified by task arithmetic, have been proven to be both effective and scalable.
We propose to merge most of the parameters while upscaling the Transformer layers to a weight-ensembling mixture of experts (MoE) module.
arXiv Detail & Related papers (2024-02-01T08:58:57Z) - Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised
Audio-Visual Video Parsing [107.031903351176]
Weakly-separated audio-visual video parsing (WS-AVVP) aims to localize the temporal extents of audio, visual and audio-visual event instances.
WS-AVVP aims to identify the corresponding event categories with only video-level category labels for training.
arXiv Detail & Related papers (2023-07-05T05:55:10Z) - MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast
and Accurate Inference on Missing Modality Sequences [32.42505193560884]
We present a novel approach named MM-Align to address the missing-modality inference problem.
MM-Align learns to capture and imitate the alignment dynamics between modality sequences.
Our method can perform more accurate and faster inference and relieve overfitting under various missing conditions.
arXiv Detail & Related papers (2022-10-23T17:44:56Z) - On Modality Bias Recognition and Reduction [70.69194431713825]
We study the modality bias problem in the context of multi-modal classification.
We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned.
Our method yields remarkable performance improvements compared with the baselines.
arXiv Detail & Related papers (2022-02-25T13:47:09Z) - Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image
Retrieval [51.42470171051007]
This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning.
By combining two fundamental learning approaches in DML, e.g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR.
We show that Modality-Aware Triplet Hard Mining (MATHM) enhances the baseline with three types of pairwise learning.
arXiv Detail & Related papers (2021-12-15T08:36:44Z) - Environmental sound analysis with mixup based multitask learning and
cross-task fusion [0.12891210250935145]
acoustic scene classification and acoustic event classification are two closely related tasks.
In this letter, a two-stage method is proposed for the above tasks.
The proposed method has confirmed the complementary characteristics of acoustic scene and acoustic event classifications.
arXiv Detail & Related papers (2021-03-30T05:11:53Z)
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.