![]() Similarity could improve methods of determining music similarity used in a variety of My use of this theoretical framework in the study of musical Similarity, but also in understanding how current audio-based extraction methods can This research not only has significant importance in our understanding of harmonic Overall, I show that a Riemannian-based approach that observes the chord labels (not using a score) enables music similarity approaches to explore audible music similarity in more depth. This thesis concludes by proposing an adapted version of Riemannian theory (removing the need for a key), which can be applied not only to computationally encoded scores, but also audio and other computationally available data (Chapters 5, and 7). Some of the apparent discrepancies in human-annotated harmony datasets specifically, the Chordify Annotator Subjectivity Dataset, a subset of Chordify’s user edit data, and my own annotation study using the song ‘Little Bit O’ Soul’ (Chapters 3, 4, and 6). Riemann’s theory is then utilised to explain I propose that we would be better able to extract high-level musical features by using traditional music-theoretical methods.įirstly, I report an initial study that highlights harmonies relevance in participants’Ĭlassification of audible music similarity. My doctoral research explores whether traditional scholarly music-theoretical methods of determining harmony (such as Riemann’s theory of harmonic function, and aspects of Schenkerian analysis) could aid in developing better methods for determining similarity. Indeed, it is surprising that such crucial applications still generally rely upon ad-hoc and proprietary methods for determining similarity. However, long-established theories of harmony such as Hugo Riemann’s theory of ‘harmonic functions’ have been under-utilised in the fields of music cognition and perception, and particularly in music information retrieval and forensic musicology. We used semantic segmentation model for transcription, which is also widely used in the field of image processing.Harmony appears to have a vital role in listeners’ perceptions of musical similarity. Colors blue, green, and red represent true-positive, false-positive, and false-negative respectively. The top row is the predicted piano roll, and the bottom row is the original label. And then make prediction of which key is in activation. This means we split time into frames, and the length of each frame is 88, corresponding to the piano rolls. And our work is the middle stage of this final goal, which we first transcribe the audio into what we called "frame level" domain. One of the main topic in AMT is to transcribe a given raw audio file into symbolic form, that is transformation from wav to midi. Just press the start button cell by cell, and you cant get the final output midi file of the given piano clip.Ī more technical way is to download this repository by executing git clone, and then enter scripts folder, modify transcribe_audio.sh, then run the script. ![]() The most straight forward way to enjoy our project is to use our colab. For more about our works, please meet our website.įor whom would interested in more technical details, the original paper is here. This work was done based on our prior work of repo1, repo2. ![]() On both dataset, we achieved the state-of-the-art results on MPE (Multi-Pitch Estimation) case frame-wisely, which on MAPS we achieved F-score 86.73%, and on MusicNet we achieved F-score 73.70%. The dataset used is MAPS and MusicNet, which the first one is a solo-piano performance collection, and the second is a multi-instrument performance collection. For the transcription, we leverage the state-of-the-art image semantic segmentation neural network and attention mechanism for transcribing piano solo, and also multi-instrument performances. This is a Automatic Music Transcription (AMT) project, aim to deal with Multi-pitch Estimation (MPE) problem, which has been a long-lasting and still a challenging problem. Music Transcription with Semantic Model Notice - A new project has been launched, which also contains this work.
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