Singing Voice Separation from Monaural Recordings using Archetypal Analysis

Ο διαχωρισμός τραγουδιστικής φωνής στοχεύει στο να διαχωρίσει το σήμα της τραγουδιστικής φωνής από το σήμα της μουσικής υπόκρουσης έχοντας ως είσοδο μουσικές ηχογραφήσεις. Η εργασία αυτή είναι ένας ακρογωνιαίος λίθος για πλήθος εργασιών που ανήκουν στην κατηγορία ”ανάκτηση μουσικής πληροφορίας” όπως για παράδειγμα αυτόματη αναγνώριση στίχων, αναγνώριση τραγουδιστή, εξόρυξη μελωδίας και ρεμίξ ήχου. Στη παρούσα διατριβή, διερευνούμε τον Διαχωρισμό τραγουδιστικής φωνής από μονοφωνικές ηχογραφήσεις εκμεταλλευόμενοι μεθόδους μη επιτηρούμενης μηχανικής μάθησης. Το κίνητρο πίσω από τις μεθόδους που χρησιμοποιήθηκαν είναι το γεγονός ότι η μουσική υπόκρουση τοποθετείται σε έναν χαμηλής-τάξης υπόχωρο λόγω του επαναλαμβανόμενου μοτίβου της, ενώ το πρότυπο της φωνής παρατηρείται ως αραιό μέσα σε ένα μουσικό κομμάτι. Συνεπώς, ανασυνθέτουμε ηχητικά φασματογραφήματα ως υπέρθεση χαμηλής-τάξης και αραιών συνιστωσών, αποτυπώνοντας τα φασματογραφήματα της μουσικής υπόκρουσης και τραγουδιστικής φωνής αντίστοιχα χρησιμοποιώντας τον αλγόριθμο Robust Principal Component Analysis. Επιπλέον, λαμβάνοντας υπόψη τη μη αρνητική φύση του μέτρου του ηχητικού φασματογραφήματος, αναπτύξαμε μία παραλλαγή της Αρχετυπικής Ανάλυσης με περιορισμούς αραιότητας στοχεύοντας να βελτιώσουμε τον διαχωρισμό. Αμφότερες οι μέθοδοι αξιολογήθηκαν στο σύνολο δεδομένων MIR-1K, το οποίο είναι κατασκευασμένο ειδικά για τον διαχωρισμό τραγουδιστικής φωνής. Τα πειραματικά αποτελέσματα δείχνουν πως και οι δύο μέθοδοι εκτελούν τον διαχωρισμό τραγουδιστικής φωνής επιτυχημένα και πετυχαίνουν στην μετρική GNSDR τιμή μεγαλύτερη των 3.0dB.Singing voice separation aims at separating the singing voice signal from the background music signal from music recordings. This task is a cornerstone for numerous MIR (Music Information Retrieval) tasks including automatic lyric recognition, singer identification, melody extraction and audio remixing. In this thesis, we investigate Singing voice separation from monaural recordings by exploiting unsupervised machine learning methods. The motivation behind the employed methods is the fact that music accompaniment lies in a low rank subspace due to its repeating motive and singing voice has a sparse pattern within the song. To this end, we decompose audio spectrograms as a superposition of low-rank components and sparse ones, capturing the spectrograms of background music and singing voice respectively using the Robust Principal Component Analysis algorithm. Furthermore, by considering the non-negative nature of the magnitude of audio spectrograms, we develop a variant of Archetypal Analysis with sparsity constraints aiming to improve the separation. Both methods are evaluated on MIR-1K dataset, which is designed especially for singing voice separation. Experimental evaluation confirms that both methods perform singing voice separation successfully and achieve a value above 3.0dB in GNSDR metric

Deep Clustering and Conventional Networks for Music Separation: Stronger Together

Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as music source separation. Contrary to conventional networks that directly estimate the source signals, deep clustering generates an embedding for each time-frequency bin, and separates sources by clustering the bins in the embedding space. We show that deep clustering outperforms conventional networks on a singing voice separation task, in both matched and mismatched conditions, even though conventional networks have the advantage of end-to-end training for best signal approximation, presumably because its more flexible objective engenders better regularization. Since the strengths of deep clustering and conventional network architectures appear complementary, we explore combining them in a single hybrid network trained via an approach akin to multi-task learning. Remarkably, the combination significantly outperforms either of its components.Comment: Published in ICASSP 201

Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask

Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a learnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the generalized Wiener filtering. This work proposes a method that learns and optimizes (during training) a source-dependent mask and does not need the aforementioned post processing step. We introduce a recurrent inference algorithm, a sparse transformation step to improve the mask generation process, and a learned denoising filter. Obtained results show an increase of 0.49 dB for the signal to distortion ratio and 0.30 dB for the signal to interference ratio, compared to previous state-of-the-art approaches for monaural singing voice separation

A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation

The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representations of the target music source(s). The spectral representations are then used to derive time-frequency masks. In this work we introduce a method to directly learn time-frequency masks from an observed mixture magnitude spectrum. We employ recurrent neural networks and train them using prior knowledge only for the magnitude spectrum of the target source. To assess the performance of the proposed method, we focus on the task of singing voice separation. The results from an objective evaluation show that our proposed method provides comparable results to deep learning based methods which operate over complicated signal representations. Compared to previous methods that approximate time-frequency masks, our method has increased performance of signal to distortion ratio by an average of 3.8 dB