Update README.md

README.md

@@ -11,19 +11,25 @@ Leveraging self-supervised deep neural networks, inductive bias, and aural skill
 
 **Date:** July 2023
 
-**Date:** July 2023
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 ## Abstract
 
 This thesis posits the existence of invariant high-level musical concepts that persist regardless of changes in sonic qualities, akin to the symbolic domain where essence endures despite varying interpretations through different performances, instruments, and styles, among many other, almost countless variables.
 
+In collaboration with Epidemic Sound AB and the Music Technology Group (MTG) at Universitat Pompeu Fabra (UPF), we employed self-supervised contrastive learning to uncover the underlying structure of Western tonal music by learning deep audio features for music boundary detection. We applied deep convolutional neural networks with a triplet loss function to identify abstract and semantic high-level musical elements without relying on their sonic qualities. This way, we replaced traditional acoustic features with deep audio embeddings, paving the way for sound-agnostic and content-sensitive music representation for downstream track segmentation tasks.
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+Our cognitively-based approach for learning embeddings focuses on using full-resolution data and preserving high-level musical information that unfolds in the time domain. A key component in our methodology is triplet networks, which effectively understand and preserve the nuanced relationships within musical data. Drawing upon our domain expertise, we developed robust transformations to encode heuristic musical concepts that should remain constant. This novel approach combines music and machine learning to enhance machine listening models’ efficacy.
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+Preliminary results suggest that, while not outperforming state-of-the-art methods, our musically-informed technique has significant potential for boundary detection tasks. Most likely, it also holds promise for nearly all downstream sound-agnostic and content-sensitive tasks constrained by data scarcity, as it is possible to achieve competitive performance compared to traditional handcrafted signal processing methods by learning only from unlabeled audio files.
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+The question remains whether such a general-purpose audio representation can mimic human hearing.
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 ### Collaboration and Methodology
 
 In collaboration with Epidemic Sound AB and the Music Technology Group (MTG) at Universitat Pompeu Fabra (UPF), we used self-supervised contrastive learning to uncover the underlying structure of Western tonal music. Our approach involved learning deep audio features to improve unsupervised music boundary detection.
 
 ### Approach
 
-Our cognitively-based approach for learning embeddings focuses on using full-resolution data and preserving high-level musical information which unfolds in the time domain. A key component in our methodology is the use of triplet networks, which provide an effective way of understanding and preserving the relative distances between different pieces of music. 
+Our cognitively-based approach for learning embeddings focuses on using full-resolution data and preserving high-level musical information that unfolds in the time domain. A key component in our methodology is the use of triplet networks, which provide an effective way of understanding and preserving the relative distances between different pieces of music. 
 
 This model structure is integral to our work, allowing us to identify and maintain the nuanced relationships within our musical data. Drawing upon our domain expertise, we developed robust transformations to encode heuristic musical concepts that should remain constant. This novel approach aims to reconcile music and machine learning, enhancing machine listening models’ efficacy through deep learning and triplet networks.