Added ISMIR papers

Author: romi1502

_posts/2024-11-08-ISMIR-desblancs.md

@@ -0,0 +1,23 @@
+---
+layout: post
+title: "From Real to Cloned Singer Identification"
+date: 2024-11-08 10:00:00 +0200
+category: Publication
+author: ddesblancs
+readtime: 1
+domains: 
+ - MIR
+people:
+ - ddesblancs
+ - gmeseguerbrocal
+ - rhennequin
+ - mmoussallam
+publication_type: conference
+publication_title: "From Real to Cloned Singer Identification"
+publication_year: 2024
+publication_authors: Dorian Desblancs, Gabriel Meseguer-Brocal, Romain Hennequin, Manuel Moussallam
+publication_conference: ISMIR
+publication_preprint: "https://arxiv.org/abs/2407.08647"
+---
+
+Cloned voices of popular singers sound increasingly realistic and have gained popularity over the past few years. They however pose a threat to the industry due to personality rights concerns. As such, methods to identify the original singer in synthetic voices are needed. In this paper, we investigate how singer identification methods could be used for such a task. We present three embedding models that are trained using a singer-level contrastive learning scheme, where positive pairs consist of segments with vocals from the same singers. These segments can be mixtures for the first model, vocals for the second, and both for the third. We demonstrate that all three models are highly capable of identifying real singers. However, their performance deteriorates when classifying cloned versions of singers in our evaluation set. This is especially true for models that use mixtures as an input. These findings highlight the need to understand the biases that exist within singer identification systems, and how they can influence the identification of voice deepfakes in music.

_posts/2024-11-08-ISMIR-kong.md

@@ -0,0 +1,22 @@
+---
+layout: post
+title: "STONE: Self-supervised Tonality Estimator"
+date: 2024-11-08 10:00:00 +0200
+category: Publication
+author: ykong
+readtime: 1
+domains: 
+ - MIR
+people:
+ - ykong
+ - gmeseguerbrocal
+ - rhennequin
+publication_type: conference
+publication_title: "STONE: Self-supervised Tonality Estimator"
+publication_year: 2024
+publication_authors: Yuexuan Kong, Vincent Lostanlen, Gabriel Meseguer-Brocal, Stella Wong, Mathieu Lagrange, Romain Hennequin
+publication_conference: ISMIR
+publication_preprint: "https://arxiv.org/abs/2407.07408"
+---
+
+Although deep neural networks can estimate the key of a musical piece, their supervision incurs a massive annotation effort. Against this shortcoming, we present STONE, the first self-supervised tonality estimator. The architecture behind STONE, named ChromaNet, is a convnet with octave equivalence which outputs a key signature profile (KSP) of 12 structured logits. First, we train ChromaNet to regress artificial pitch transpositions between any two unlabeled musical excerpts from the same audio track, as measured as cross-power spectral density (CPSD) within the circle of fifths (CoF). We observe that this self-supervised pretext task leads KSP to correlate with tonal key signature. Based on this observation, we extend STONE to output a structured KSP of 24 logits, and introduce supervision so as to disambiguate major versus minor keys sharing the same key signature. Applying different amounts of supervision yields semi-supervised and fully supervised tonality estimators: i.e., Semi-TONEs and Sup-TONEs. We evaluate these estimators on FMAK, a new dataset of 5489 real-world musical recordings with expert annotation of 24 major and minor keys. We find that Semi-TONE matches the classification accuracy of Sup-TONE with reduced supervision and outperforms it with equal supervision.