Fine-grained music source separation with deep learning models

音频源分离是将音频信号分离为每个信号源的单独信号的任务。对于音乐，信号源是出现在音轨中的乐器，例如吉他、贝司、钢琴、鼓和人声。音乐源分离是音乐信息检索中的一项重要任务，它可以在任意的音乐曲目上实现不同的应用。例如，在音乐教育的背景下，为学生创建播放曲目，促进相关乐器的耳转录，或自动创建卡拉OK背景曲目。目前的商业软件只专注于分裂声乐，低音，和鼓从音频文件，仍然没有产品，可以同时分离更多的乐器在质量，可用于音乐家混音/重制目标。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，并将其提取到独立的曲目中，而不会有质量损失，包括声乐，器乐，鼓，贝司，钢琴，电吉他，原声吉他，合成器等，该项目将由AudioStrip领导，一家总部位于伦敦的MusicTech公司，专注于构建ML工具，为有执照的DJ腾出时间。专注于音乐源分离的制作人和音乐企业。该项目将与伦敦玛丽皇后大学的数字音乐中心合作，该中心是英国最大的研究中心之一，专注于连接音乐和人工智能。

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.