Automatic Chord Estimation Based on a Frame-wise Convolutional Recurrent Neural Network with Non-Aligned Annotations

International audienceThis paper describes a weakly-supervised approach to Automatic Chord Estimation (ACE) task that aims to estimate a sequence of chords from a given music audio signal at the frame level, under a realistic condition that only non-aligned chord annotations are available. In conventional studies assuming the availability of time-aligned chord annotations, Deep Neural Networks (DNNs) that learn frame-wise mappings from acoustic features to chords have attained excellent performance. The major drawback of such frame-wise models is that they cannot be trained without the time alignment information. Inspired by a common approach in automatic speech recognition based on non-aligned speech transcriptions, we propose a two-step method that trains a Hidden Markov Model (HMM) for the forced alignment between chord annotations and music signals, and then trains a powerful frame-wise DNN model for ACE. Experimental results show that although the frame-level accuracy of the forced alignment was just under 90%, the performance of the proposed method was degraded only slightly from that of the DNN model trained by using the ground-truth alignment data. Furthermore, using a sufficient amount of easily collected non-aligned data, the proposed method is able to reach or even outperform the conventional methods based on ground-truth time-aligned annotations

Using musical relationships between chord labels in automatic chord extraction tasks

International audienceRecent research on Automatic Chord Extraction (ACE) has focused on the improvement of models based on machine learning. However, most models still fail to take into account the prior knowledge underlying the labeling alphabets (chord labels). Furthermore, recent works have shown that ACE performances have reached a glass ceiling.Therefore, this prompts the need to focus on other aspects of the task, such as the introduction of musical knowledge in the representation, the improvement of the models towards more complex chord alphabets and the development of more adapted evaluation methods.In this paper, we propose to exploit specific properties and relationships between chord labels in order to improve the learning of statistical ACE models. Hence, we analyze the interdependence of the representations of chords and their associated distances, the precision of the chord alphabets, and the impact of performing alphabet reductionbefore or after training the model. Furthermore, we propose new training losses based on musical theory. We show that these improve the results of ACE systems based on Convolutional Neural Networks. By analyzing our results, we uncover a set of related insights on ACE tasks based on statistical models, and also formalize the musicalmeaning of some classification errors

Artificial Intelligence Music Generators in Real Time Jazz Improvisation: a performer’s view

Μια αμφιλεγόμενη είσοδος γεννητριών μουσικής τεχνητής νοημοσύνης στον κόσμο της μουσικής σύνθεσης και ερμηνείας καλπάζει επί του παρόντος. Γόνιμη έρευνα που πηγάζει απο τομείς όπως η ανάκτηση πληροφοριών μουσικής, τα νευρονικά δίκτυα και η βαθιά μάθηση, μεταξύ άλλων, διαμορφώνει αυτό το μέλλον. Ενσωματωμένα και μη ενσωματωμένα συστήματα τεχνητής νοημοσύνης έχουν εισέλθει στον κόσμο της τζαζ προκειμένου να συνδημιουργήσουν ιδιωματικούς μουσικούς αυτοσχεδιασμούς. Αυτή η διπλωματική εξετάζει τους προκύπτοντες μελωδικούς αυτοσχεδιασμούς που παράγονται από τις γεννήτριες OMax, ImproteK και Djazz (OID) μέσω του φακού των στοιχείων της μουσικής και το κάνει από την άποψη ενός ερμηνευτή. Η ανάλυση βασίζεται κυρίως στην αξιολόγηση των ήδη δημοσιευμένων αποτελεσμάτων, καθώς και σε μια μελέτη περίπτωσης που πραγματοποίηθηκε κατά την ολοκλήρωση αυτής της εργασίας που περιλαμβάνει την απόδοση, την ακρόαση και την αξιολόγηση των παραγόμενων αυτοσχεδιασμών του OMax. Επίσης, η εργασία ασχολείται με φιλοσοφικά ζητήματα, με τα γνωστικά θεμέλια του συναισθήματος και του νοήματος και παρέχει μια ολοκληρωμένη ανάλυση της λειτουργικότητας του OID.A highly controversial entrance of Artificial Intelligence (AI) music generators in the world of music composition and performance is currently advancing. A fruitful research from Music Information Retrieval, Neural Networks and Deep Learning, among other areas, are shaping this future. Embodied and non-embodied AI systems have stepped into the world of jazz in order to co-create idiomatic music improvisations. But how musical these improvisations are? This dissertation looks at the resulted melodic improvisations produced by OMax, ImproteK and Djazz (OID) AI generators through the lens of the elements of music and it does so from a performer’s point of view. The analysis is based mainly on the evaluation of already published results as well as on a case study I carried out during the completion of this essay which includes performance, listening and evaluation of generated improvisations of OMax. The essay also reflects upon philosophical issues, cognitive foundations of emotion and meaning and provides a comprehensive analysis of the functionality of OID

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f