Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music

• URL: http://arxiv.org/abs/2501.06959v1
• Date: Sun, 12 Jan 2025 22:39:58 GMT
• Title: Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music
• Authors: Venkatakrishnan Vaidyanathapuram Krishnan, Noel Alben, Anish Nair, Nathaniel Condit-Schultz,
• Abstract summary: Music source separation demixes a piece of music into its individual sound sources. Most commonly available datasets are made from commercial Western music. 'Sanidha' is the first open-source novel dataset for Carnatic music.
• Score: 0.8437187555622164
• License:
• Abstract: Music source separation demixes a piece of music into its individual sound sources (vocals, percussion, melodic instruments, etc.), a task with no simple mathematical solution. It requires deep learning methods involving training on large datasets of isolated music stems. The most commonly available datasets are made from commercial Western music, limiting the models' applications to non-Western genres like Carnatic music. Carnatic music is a live tradition, with the available multi-track recordings containing overlapping sounds and bleeds between the sources. This poses a challenge to commercially available source separation models like Spleeter and Hybrid Demucs. In this work, we introduce 'Sanidha', the first open-source novel dataset for Carnatic music, offering studio-quality, multi-track recordings with minimal to no overlap or bleed. Along with the audio files, we provide high-definition videos of the artists' performances. Additionally, we fine-tuned Spleeter, one of the most commonly used source separation models, on our dataset and observed improved SDR performance compared to fine-tuning on a pre-existing Carnatic multi-track dataset. The outputs of the fine-tuned model with 'Sanidha' are evaluated through a listening study.

Related papers

• Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset [0.29998889086656577]
  We show that a convolutional neural network trained on an artificial dataset can identify real-world samples in commercial hip-hop music. We optimize the model using a joint classification and metric learning loss and show that it achieves 13% greater precision on real-world instances of sampling.
  arXiv  Detail & Related papers  (2025-02-10T11:30:35Z)
• Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks [18.95453617434051]
  Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. New music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods.
  arXiv  Detail & Related papers  (2024-09-13T17:53:06Z)
• Benchmarking Sub-Genre Classification For Mainstage Dance Music [6.042939894766715]
  This work introduces a novel benchmark comprising a new dataset and a baseline. Our dataset extends the number of sub-genres to cover most recent mainstage live sets by top DJs worldwide in music festivals. For the baseline, we developed deep learning models that outperform current state-of-the-art multimodel language models.
  arXiv  Detail & Related papers  (2024-09-10T17:54:00Z)
• Multi-Source Music Generation with Latent Diffusion [7.832209959041259]
  Multi-Source Diffusion Model (MSDM) proposed to model music as a mixture of multiple instrumental sources. MSLDM employs Variational Autoencoders (VAEs) to encode each instrumental source into a distinct latent representation. This approach significantly enhances the total and partial generation of music.
  arXiv  Detail & Related papers  (2024-09-10T03:41:10Z)
• VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling [71.01050359126141]
  We propose VidMuse, a framework for generating music aligned with video inputs. VidMuse produces high-fidelity music that is both acoustically and semantically aligned with the video.
  arXiv  Detail & Related papers  (2024-06-06T17:58:11Z)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• From West to East: Who can understand the music of the others better? [91.78564268397139]
  We leverage transfer learning methods to derive insights about similarities between different music cultures. We use two Western music datasets, two traditional/folk datasets coming from eastern Mediterranean cultures, and two datasets belonging to Indian art music. Three deep audio embedding models are trained and transferred across domains, including two CNN-based and a Transformer-based architecture, to perform auto-tagging for each target domain dataset.
  arXiv  Detail & Related papers  (2023-07-19T07:29:14Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• Benchmarks and leaderboards for sound demixing tasks [44.99833362998488]
  We introduce two new benchmarks for the sound source separation tasks. We compare popular models for sound demixing, as well as their ensembles, on these benchmarks. We also develop a novel approach for audio separation, based on the ensembling of different models that are suited best for the particular stem.
  arXiv  Detail & Related papers  (2023-05-12T14:00:26Z)
• 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)
• Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task [86.72661027591394]
  We generate complete and semantically consistent symbolic music scores from text descriptions. We explore the efficacy of using publicly available checkpoints for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity.
  arXiv  Detail & Related papers  (2022-11-21T07:19:17Z)

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.