Investigating computational models of perceptual attack time

The perceptual attack time (PAT) is the compensation for differing attack components of sounds, in the case of seeking a perceptually isochronous presentation of sounds. It has applications in scheduling and is related to, but not necessarily the same as, the moment of perceptual onset. This paper describes a computational investigation of PAT over a set of 25 synthesised stimuli, and a larger database of 100 sounds equally divided into synthesised and ecological. Ground truth PATs for modeling were obtained by the alternating presentation paradigm, where subjects adjusted the relative start time of a reference click and the sound to be judged. Whilst fitting experimental data from the 25 sound set was plausible, difficulties with existing models were found in the case of the larger test set. A pragmatic solution was obtained using a neural net architecture. In general, learnt schema of sound classification may be implicated in resolving the multiple detection cues evoked by complex sounds

LinguaTag: an Emotional Speech Analysis Application

The analysis of speech, particularly for emotional content, is an open area of current research. Ongoing work has developed an emotional speech corpus for analysis, and defined a vowel stress method by which this analysis may be performed. This paper documents the development of LinguaTag, an open source speech analysis software application which implements this vowel stress emotional speech analysis method developed as part of research into the acoustic and linguistic correlates of emotional speech. The analysis output is contained within a file format combining SMIL and SSML markup tags, to facilitate search and retrieval methods within an emotional speech corpus database. In this manner, analysis performed using LinguaTag aims to combine acoustic, emotional and linguistic descriptors in a single metadata framework

MUSIC RECOMMENDATION SYSTEM BASED ON COSINE SIMILARITY AND SUPERVISED GENRE CLASSIFICATION

Categorizing musical styles can be useful in solving various practical problems, such as establishing musical relationships between songs, similar songs, and finding communities that share an interest in a particular genre. Our goal in this research is to determine the most effective machine learning technique to accurately predict song genres using the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms. In addition, this article offers a contrastive examination of the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) when dimensioning is considered and without using Principal Component Analysis (PCA) for dimension reduction. MFCC is used to collect data from datasets. In addition, each track uses the MFCC feature. The results reveal that the K-Nearest Neighbors and Support Vector Machine offer more precise results without reducing dimensions than PCA results. The accuracy of using the PCA method is 58% and has the potential to decrease. In this music genre classification, K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) are proven to be more efficient classifiers. K-Nearest Neighbors accuracy is 64,9%, and Support Vector Machine (SVM) accuracy is 77%. Not only that, but we also created a recommender system using cosine similarity to provide recommendations for songs that have relatively the same genre. From one sample of the songs tested, five songs were obtained that had the same genre with an average accuracy of 80%

Automated generation of movie tributes

O objetivo desta tese é gerar um tributo a um filme sob a forma de videoclip, considerando como entrada um filme e um segmento musical coerente. Um tributo é considerado um vídeo que contém os clips mais significativos de um filme, reproduzidos sequencialmente, enquanto uma música toca. Nesta proposta, os clips a constar do tributo final são o resultado da sumarização das legendas do filme com um algoritmo de sumarização genérico. É importante que o artefacto seja coerente e fluido, pelo que há a necessidade de haver um equilíbrio entre a seleção de conteúdo importante e a seleção de conteúdo que esteja em harmonia com a música. Para tal, os clips são filtrados de forma a garantir que apenas aqueles que contêm a mesma emoção da música aparecem no vídeo final. Tal é feito através da extração de vetores de características áudio relacionadas com emoções das cenas às quais os clips pertencem e da música, e, de seguida, da sua comparação por meio do cálculo de uma medida de distância. Por fim, os clips filtrados preenchem a música cronologicamente. Os resultados foram positivos: em média, os tributos produzidos obtiveram 7 pontos, numa escala de 0 a 10, em critérios como seleção de conteúdo e coerência emocional, fruto de avaliação humana.This thesis’ purpose is to generate a movie tribute in the form of a videoclip for a given movie and music. A tribute is considered to be a video containing meaningful clips from the movie playing along with a cohesive music piece. In this work, we collect the clips by summarizing the movie subtitles with a generic summarization algorithm. It is important that the artifact is coherent and fluid, hence there is the need to balance between the selection of important content and the selection of content that is in harmony with the music. To achieve so, clips are filtered so as to ensure that only those that contain the same emotion as the music are chosen to appear in the final video. This is made by extracting vectors of emotion-related audio features from the scenes they belong to and from the music, and then comparing them with a distance measure. Finally, filtered clips fill the music length in a chronological order. Results were positive: on average, the produced tributes obtained scores of 7, on a scale from 0 to 10, on content selection, and emotional coherence criteria, from human evaluation

Music and language expressiveness: When emotional character does not suffice: the dimension of expressiveness in the cognitive processing of music and language

Book synopsis: In recent decades, the relationship between music, emotions, health and well-being has become a hot topic. Scientific research and new neuro-imaging technologies have provided extraordinary new insights into how music affects our brains and bodies, and researchers in fields ranging from psychology and music therapy to history and sociology have turned their attention to the question of how music relates to mind, body, feelings and health, generating a wealth of insights as well as new challenges. Yet this work is often divided by discipline and methodology, resulting in parallel, yet separate discourses. In this context, The Routledge Companion to Music, Mind and Well-being seeks to foster truly interdisciplinary approaches to key questions about the nature of musical experience and to demonstrate the importance of the conceptual and ideological frameworks underlying research in this field. Incorporating perspectives from musicology, history, psychology, neuroscience, music education, philosophy, sociology, linguistics and music therapy, this volume opens the way for a generative dialogue across both scientific and humanistic scholarship. The Companion is divided into two sections. The chapters in the first, historical section consider the varied ways in which music, the emotions, well-being and their interactions have been understood in the past, from Antiquity to the twentieth century, shedding light on the intellectual origins of debates that continue today. The chapters in the second, contemporary section offer a variety of current scientific perspectives on these topics and engage wider philosophical problems. The Companion ends with chapters that explore the practical application of music in healthcare, education and welfare, drawing on work on music as a social and ecological phenomenon. Contextualising contemporary scientific research on music within the history of ideas, this volume provides a unique overview of what it means to study music in relation to the mind and well-being

Enhancing film sound design using audio features, regression models and artificial neural networks

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of New Music Research on 21/09/2021, available online: https://doi.org/10.1080/09298215.2021.1977336Making the link between human emotion and music is challenging. Our aim was to produce an efficient system that emotionally rates songs from multiple genres. To achieve this, we employed a series of online self-report studies, utilising Russell's circumplex model. The first study (n = 44) identified audio features that map to arousal and valence for 20 songs. From this, we constructed a set of linear regressors. The second study (n = 158) measured the efficacy of our system, utilising 40 new songs to create a ground truth. Results show our approach may be effective at emotionally rating music, particularly in the prediction of valence