And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?

Version identification (VI) has seen substantial progress over the past few years. On the one hand, the introduction of the metric learning paradigm has favored the emergence of scalable yet accurate VI systems. On the other hand, using features focusing on specific aspects of musical pieces, such as melody, harmony, or lyrics, yielded interpretable and promising performances. In this work, we build upon these recent advances and propose a metric learning-based system systematically leveraging four dimensions commonly admitted to convey musical similarity between versions: melodic line, harmonic structure, rhythmic patterns, and lyrics. We describe our deliberately simple model architecture, and we show in particular that an approximated representation of the lyrics is an efficient proxy to discriminate between versions and non-versions. We then describe how these features complement each other and yield new state-of-the-art performances on two publicly available datasets. We finally suggest that a VI system using a combination of melodic, harmonic, rhythmic and lyrics features could theoretically reach the optimal performances obtainable on these datasets

Detecting cover songs with pitch class key-invariant networks

Deep Learning (DL) has recently been applied successfully to the task of Cover Song Identification (CSI). Meanwhile, neural networks that consider music signal data structure in their design have been developed. In this paper, we propose a Pitch Class Key-Invariant Network, PiCKINet, for CSI. Like some other CSI networks, PiCKINet inputs a Constant-Q Transform (CQT) pitch feature. Unlike other such networks, large multi-octave kernels produce a latent representation with pitch class dimensions that are maintained throughout PiCKINet by key-invariant convolutions. PiCKINet is seen to be more effective, and efficient, than other CQT-based networks. We also propose an extended variant, PiCKINet+, that employs a centre loss penalty, squeeze and excite units, and octave swapping data augmentation. PiCKINet+ shows an improvement of ~17% MAP relative to the well-known CQTNet when tested on a set of ~16K tracks

Harmonic Change Detection from Musical Audio

In this dissertation, we advance an enhanced method for computing Harte et al.’s [31] Harmonic Change Detection Function (HCDF). HCDF aims to detect harmonic transitions in musical audio signals. HCDF is crucial both for the chord recognition in Music Information Retrieval (MIR) and a wide range of creative applications. In light of recent advances in harmonic description and transformation, we depart from the original architecture of Harte et al.’s HCDF, to revisit each one of its component blocks, which are evaluated using an exhaustive grid search aimed to identify optimal parameters across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes, by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at > 99%, our method improves precision by 6.28%. Aiming to leverage novel strategies for real-time harmonic-content audio processing, the optimized HCDF is made available for Javascript and the MAX and Pure Data multimedia programming environments. Moreover, all the data as well as the Python code used to generate them, are made available.<br /

Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzüberlappung

Everyday audio recordings involve mixture signals: music contains a mixture of instruments; in a meeting or conference, there is a mixture of human voices. For these mixtures, automatically separating or estimating the number of sources is a challenging task. A common assumption when processing mixtures in the time-frequency domain is that sources are not fully overlapped. However, in this work we consider some cases where the overlap is severe — for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") — highlighting the need for new representations and more powerful models. To address the problems of source separation and count estimation, we use conventional signal processing techniques as well as deep neural networks (DNN). We first address the source separation problem for unison instrument mixtures, studying the distinct spectro-temporal modulations caused by vibrato. To exploit these modulations, we developed a method based on time warping, informed by an estimate of the fundamental frequency. For cases where such estimates are not available, we present an unsupervised model, inspired by the way humans group time-varying sources (common fate). This contribution comes with a novel representation that improves separation for overlapped and modulated sources on unison mixtures but also improves vocal and accompaniment separation when used as an input for a DNN model. Then, we focus on estimating the number of sources in a mixture, which is important for real-world scenarios. Our work on count estimation was motivated by a study on how humans can address this task, which lead us to conduct listening experiments, confirming that humans are only able to estimate the number of up to four sources correctly. To answer the question of whether machines can perform similarly, we present a DNN architecture, trained to estimate the number of concurrent speakers. Our results show improvements compared to other methods, and the model even outperformed humans on the same task. In both the source separation and source count estimation tasks, the key contribution of this thesis is the concept of “modulation”, which is important to computationally mimic human performance. Our proposed Common Fate Transform is an adequate representation to disentangle overlapping signals for separation, and an inspection of our DNN count estimation model revealed that it proceeds to find modulation-like intermediate features.Im Alltag sind wir von gemischten Signalen umgeben: Musik besteht aus einer Mischung von Instrumenten; in einem Meeting oder auf einer Konferenz sind wir einer Mischung menschlicher Stimmen ausgesetzt. Für diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häufige Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig überlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Überlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) —, so dass neue Signal-Repräsentationen und leistungsfähigere Modelle notwendig sind. Um die zwei genannten Probleme zu bewältigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunächst auf das Problem der Quellentrennung für Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelösten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Für Fälle, in denen diese Schätzungen nicht verfügbar sind, stellen wir ein unüberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitveränderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthält eine neuartige Repräsentation, die die Separierbarkeit für überlappte und modulierte Quellen in Unisono-Mischungen erhöht, aber auch die Trennung in Gesang und Begleitung verbessert, wenn sie in einem DNN-Modell verwendet wird. Im Weiteren beschäftigen wir uns mit der Schätzung der Anzahl von Quellen in einer Mischung, was für reale Szenarien wichtig ist. Unsere Arbeit an der Schätzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzuführen, die bestätigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschätzen. Um nun die Frage zu beantworten, ob Maschinen dies ähnlich gut können, stellen wir eine DNN-Architektur vor, die erlernt hat, die Anzahl der gleichzeitig sprechenden Sprecher zu ermitteln. Die Ergebnisse zeigen Verbesserungen im Vergleich zu anderen Methoden, aber vor allem auch im Vergleich zu menschlichen Hörern. Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der “Modulation”, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Überlappung von Signalen für die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schließlich, dass sich auch hier modulationsähnliche Merkmale finden lassen

Visual context for verb sense disambiguation and multilingual representation learning

Every day billions of images are uploaded to the web. To process images at such a large scale it is important to build automatic image understanding systems. An important step towards understanding the content of the images is to be able to understand all the objects, scenes and actions depicted in the image. These systems should be capable of integrating with natural language or text to be able to query and interact with humans for tasks such as image retrieval. Verbs play a key role in the understanding of sentences and scenes. Verbs express the semantics of an actions as well as the interactions between objects participating in an event. Thus understanding verbs is central to both language and image understanding. However, verbs are known for their variability in meaning with context. Many studies in psychology have shown that contextual information plays an important role in semantic understanding and processing in the human visual system. We use this as intuition and understand the role of textual or visual context in tasks that combine language and vision. Our research presented in this thesis focuses on the problems of integrating visual and textual contexts for: (i) automatically identifying verbs that denote actions depicted in the images; (ii) fine-grained analysis of how visual context can help disambiguate different meanings of verbs in a language or across languages; (iii) the role played by the visual and multilingual context in learning representations that allow us to query information across modalities and languages. First, we propose the task of visual sense disambiguation, an alternative way of addressing the action recognition task. Instead of identifying the actions directly, we develop a two step process: identifying the verb that denotes the action being depicted in an image and then disambiguate the meaning of the verb based on the visual and textual context associated with the image. We first build a image-verb classifier based on the weak signal from image description data and analyse the specific regions that model focuses on while predicting the verb. We then disambiguate the meaning of the verb shown in the image using image features and sense-inventories. We test the hypothesis that visual and textual context associated with the image contribute to the disambiguation task. Second, we ask whether the predictions made by such models correspond to human intuitions about visual verbs or actions. We analyse whether the image regions a verb prediction model identifies as salient for a given verb correlate with the regions fixated by human observers performing an action classification task. We also compare the correlation of human fixations against visual saliency and center bias models. Third, we propose the crosslingual verb disambiguation task: identifying the correct translation of the verb in a target language based on visual context. This task has the potential to resolve lexical ambiguity in machine translation when the visual context is available. We propose a series of models and show that multimodal models that fuse textual information with visual features have an edge over text or visual only models. We then demonstrate how visual sense disambiguation can be combined with lexical constraint decoding to improve the performance of a standard unimodal machine translation system on image descriptions. Finally, we move on to learn joint representations for images and text in multiple languages. We test the hypothesis that context provided as visual information or text in other language contributes to better representation learning. We propose models to map text from multiple languages and images into a common space and evaluating the usefulness of the second language in multimodal search and usefulness of image in the crosslingual search. Our experiments suggest that exploiting multilingual and multimodal resources can help in learning better semantic representations that are useful for various multimodal natural language understanding tasks. Our experiments on visual sense disambiguation, sense disambiguation across languages, multimodal and cross-lingual search demonstrate that visual context alone or combined with textual context is useful for enhancing multimodal and crosslingual applications