Font Representation Learning via Paired-glyph Matching

Fonts can convey profound meanings of words in various forms of glyphs. Without typography knowledge, manually selecting an appropriate font or designing a new font is a tedious and painful task. To allow users to explore vast font styles and create new font styles, font retrieval and font style transfer methods have been proposed. These tasks increase the need for learning high-quality font representations. Therefore, we propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model that attracts the representations of glyphs in the same font to one another, but pushes away those of other fonts. Through evaluations on font retrieval with query glyphs on new fonts, we show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques. Finally on the downstream font style transfer and generation tasks, we confirm the benefits of transfer learning with the proposed method. The source code is available at https://github.com/junhocho/paired-glyph-matching.Comment: Accepted to BMVC202

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations

Audio representation learning based on deep neural networks (DNNs) emerged as an alternative approach to hand-crafted features. For achieving high performance, DNNs often need a large amount of annotated data which can be difficult and costly to obtain. In this paper, we propose a method for learning audio representations, aligning the learned latent representations of audio and associated tags. Aligning is done by maximizing the agreement of the latent representations of audio and tags, using a contrastive loss. The result is an audio embedding model which reflects acoustic and semantic characteristics of sounds. We evaluate the quality of our embedding model, measuring its performance as a feature extractor on three different tasks (namely, sound event recognition, and music genre and musical instrument classification), and investigate what type of characteristics the model captures. Our results are promising, sometimes in par with the state-of-the-art in the considered tasks and the embeddings produced with our method are well correlated with some acoustic descriptors.Comment: 8 pages, 1 figure, workshop on Self-supervision in Audio and Speech at the 37th International Conference on Machine Learning (ICML), 2020, Vienna, Austri

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.Comment: to appear in ACM Computing Survey

Statistical distribution of common audio features : encounters in a heavy-tailed universe

In the last few years some Music Information Retrieval (MIR) researchers have spotted important drawbacks in applying standard successful-in-monophonic algorithms to polyphonic music classification and similarity assessment. Noticeably, these so called “Bag-of-Frames” (BoF) algorithms share a common set of assumptions. These assumptions are substantiated in the belief that the numerical descriptions extracted from short-time audio excerpts (or frames) are enough to capture relevant information for the task at hand, that these frame-based audio descriptors are time independent, and that descriptor frames are well described by Gaussian statistics. Thus, if we want to improve current BoF algorithms we could: i) improve current audio descriptors, ii) include temporal information within algorithms working with polyphonic music, and iii) study and characterize the real statistical properties of these frame-based audio descriptors. From a literature review, we have detected that many works focus on the first two improvements, but surprisingly, there is a lack of research in the third one. Therefore, in this thesis we analyze and characterize the statistical distribution of common audio descriptors of timbre, tonal and loudness information. Contrary to what is usually assumed, our work shows that the studied descriptors are heavy-tailed distributed and thus, they do not belong to a Gaussian universe. This new knowledge led us to propose new algorithms that show improvements over the BoF approach in current MIR tasks such as genre classification, instrument detection, and automatic tagging of music. Furthermore, we also address new MIR tasks such as measuring the temporal evolution of Western popular music. Finally, we highlight some promising paths for future audio-content MIR research that will inhabit a heavy-tailed universe.En el campo de la extracción de información musical o Music Information Retrieval (MIR), los algoritmos llamados Bag-of-Frames (BoF) han sido aplicados con éxito en la clasificación y evaluación de similitud de señales de audio monofónicas. Por otra parte, investigaciones recientes han señalado problemas importantes a la hora de aplicar dichos algoritmos a señales de música polifónica. Estos algoritmos suponen que las descripciones numéricas extraídas de los fragmentos de audio de corta duración (o frames ) son capaces de capturar la información necesaria para la realización de las tareas planteadas, que el orden temporal de estos fragmentos de audio es irrelevante y que las descripciones extraídas de los segmentos de audio pueden ser correctamente descritas usando estadísticas Gaussianas. Por lo tanto, si se pretende mejorar los algoritmos BoF actuales se podría intentar: i) mejorar los descriptores de audio, ii) incluir información temporal en los algoritmos que trabajan con música polifónica y iii) estudiar y caracterizar las propiedades estadísticas reales de los descriptores de audio. La bibliografía actual sobre el tema refleja la existencia de un número considerable de trabajos centrados en las dos primeras opciones de mejora, pero sorprendentemente, hay una carencia de trabajos de investigación focalizados en la tercera opción. Por lo tanto, esta tesis se centra en el análisis y caracterización de la distribución estadística de descriptores de audio comúnmente utilizados para representar información tímbrica, tonal y de volumen. Al contrario de lo que se asume habitualmente, nuestro trabajo muestra que los descriptores de audio estudiados se distribuyen de acuerdo a una distribución de “cola pesada” y por lo tanto no pertenecen a un universo Gaussiano. Este descubrimiento nos permite proponer nuevos algoritmos que evidencian mejoras importantes sobre los algoritmos BoF actualmente utilizados en diversas tareas de MIR tales como clasificación de género, detección de instrumentos musicales y etiquetado automático de música. También nos permite proponer nuevas tareas tales como la medición de la evolución temporal de la música popular occidental. Finalmente, presentamos algunas prometedoras líneas de investigación para tareas de MIR ubicadas, a partir de ahora, en un universo de “cola pesada”.En l’àmbit de la extracció de la informació musical o Music Information Retrieval (MIR), els algorismes anomenats Bag-of-Frames (BoF) han estat aplicats amb èxit en la classificació i avaluació de similitud entre senyals monofòniques. D’altra banda, investigacions recents han assenyalat importants inconvenients a l’hora d’aplicar aquests mateixos algorismes en senyals de música polifònica. Aquests algorismes BoF suposen que les descripcions numèriques extretes dels fragments d’àudio de curta durada (frames) son suficients per capturar la informació rellevant per als algorismes, que els descriptors basats en els fragments son independents del temps i que l’estadística Gaussiana descriu correctament aquests descriptors. Per a millorar els algorismes BoF actuals doncs, es poden i) millorar els descriptors, ii) incorporar informació temporal dins els algorismes que treballen amb música polifònica i iii) estudiar i caracteritzar les propietats estadístiques reals d’aquests descriptors basats en fragments d’àudio. Sorprenentment, de la revisió bibliogràfica es desprèn que la majoria d’investigacions s’han centrat en els dos primers punts de millora mentre que hi ha una mancança quant a la recerca en l’àmbit del tercer punt. És per això que en aquesta tesi, s’analitza i caracteritza la distribució estadística dels descriptors més comuns de timbre, to i volum. El nostre treball mostra que contràriament al què s’assumeix, els descriptors no pertanyen a l’univers Gaussià sinó que es distribueixen segons una distribució de “cua pesada”. Aquest descobriment ens permet proposar nous algorismes que evidencien millores importants sobre els algorismes BoF utilitzats actualment en diferents tasques com la classificació del gènere, la detecció d’instruments musicals i l’etiquetatge automàtic de música. Ens permet també proposar noves tasques com la mesura de l’evolució temporal de la música popular occidental. Finalment, presentem algunes prometedores línies d’investigació per a tasques de MIR ubicades a partir d’ara en un univers de “cua pesada”

Automatic characterization and generation of music loops and instrument samples for electronic music production

Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation