Fine-grained music source separation with deep learning models

利用深度学习模型进行细粒度音乐源分离

• 批准号: 10107090
• 金额: $ 18.67万
• 依托单位: AUDIOSTRIP LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10107090
• 关键词: Fine; grained; music; source; separation

Audio source separation is the task of splitting an audio signal into separate signals for each signal source. For music, the signal sources are the instruments that appear in the track, e.g. guitar, bass, piano, drums, and vocals. As an important task in music information retrieval, music source separation enables diverse applications on arbitrary music tracks that would need manual creation of stems otherwise. For example, in the context of music education, the creation of play-along tracks for students, facilitating by-ear transcription of relevant instruments, or automatic creation of karaoke backing tracks.Current commercial software only focuses on splitting vocal, bass, and drums from the audio files and there is still no product that can simultaneously separate more instruments in a quality that is usable by musicians for remixing/remastering objectives. The project will fill this gap by developing advanced machine-learning algorithms for high-quality audio source separation. The ultimate goal is to automatically identify musical elements from any given song and extract them into independent tracks without quality loss, including vocal, instrumental, drums, bass, piano, electric guitar, acoustic guitar, synthesizer, etc.The project will be led by AudioStrip, a London-based MusicTech company with a focus on building ML tools to free up time for licensed DJs/Producers and music businesses with a central focus on music source separation. The project will be collaborated with the Center for Digital Music at Queen Mary University of London, one of the biggest research centers in the UK focused on bridging music and artificial intelligence.

音频源分离是将音频信号分割成每个信号源的单独信号的任务。对于音乐，信号源是出现在曲目中的乐器，如吉他、贝斯、钢琴、鼓和人声。作为音乐信息检索中的一项重要任务，音乐源分离可以实现对任意音乐轨迹的多样化应用，否则需要手动创建词干。例如，在音乐教育方面，为学生创建播放曲目，促进相关乐器的耳边转录，或自动创建卡拉OK配乐曲目。目前的商业软件仅专注于从音频文件中分离人声、贝斯和鼓，目前还没有产品可以同时分离更多的乐器，其质量可供音乐家用于混音/重新主曲目标。该项目将通过开发用于高质量音频源分离的先进机器学习算法来填补这一空白。最终目标是从任何给定的歌曲中自动识别音乐元素，并将它们提取到独立的曲目中，而不会造成质量损失，包括人声、器乐、鼓、贝斯、钢琴、电吉他、原声吉他、合成器等。该项目将由AudioZone领导，这是一家总部位于伦敦的MusicTech公司，专注于构建ML工具，为获得许可的DJ/制作人和音乐企业腾出时间，主要关注音乐来源分离。该项目将与伦敦玛丽女王大学数字音乐中心合作，该中心是英国最大的专注于连接音乐和人工智能的研究中心之一。