Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art

Effort in gestural interactions with imaginary objects in Hindustani Dhrupad vocal music

Physical effort has often been regarded as a key factor of expressivity in music performance. Nevertheless, systematic experimental approaches to the subject have been rare. In North Indian classical (Hindustani) vocal music, singers often engage with melodic ideas during improvisation by manipulating intangible, imaginary objects with their hands, such as through stretching, pulling, pushing, throwing etc. The above observation suggests that some patterns of change in acoustic features allude to interactions that real objects through their physical properties can afford. The present study reports on the exploration of the relationships between movement and sound by accounting for the physical effort that such interactions require in the Dhrupad genre of Hindustani vocal improvisation. The work follows a mixed methodological approach, combining qualitative and quantitative methods to analyse interviews, audio-visual material and movement data. Findings indicate that despite the flexibility in the way a Dhrupad vocalist might use his/her hands while singing, there is a certain degree of consistency by which performers associate effort levels with melody and types of gestural interactions with imaginary objects. However, different schemes of cross-modal associations are revealed for the vocalists analysed, that depend on the pitch space organisation of each particular melodic mode (rāga), the mechanical requirements of voice production, the macro-structure of the ālāp improvisation and morphological cross-domain analogies. Results further suggest that a good part of the variance in both physical effort and gesture type can be explained through a small set of sound and movement features. Based on the findings, I argue that gesturing in Dhrupad singing is guided by: the know-how of humans in interacting with and exerting effort on real objects of the environment, the movement–sound relationships transmitted from teacher to student in the oral music training context and the mechanical demands of vocalisation

Towards a better understanding of music playlist titles and descriptions

Music playlists, either user-generated or curated by music streaming services, often come with titles and descriptions. Although informative, these titles and descriptions make up a sparse and noisy semantic space that is challenging to be leveraged for tasks such as making music recommendations. This dissertation is dedicated to developing a better understanding of playlist titles and descriptions by leveraging track sequences in playlists. Specifically, work has been done to capture latent patterns in tracks by an embedding approach, and the latent patterns are found to be well aligned with the organizing principles of mixtapes identified more than a decade ago. The effectiveness of the latent patterns is evaluated by the task of generating descriptive keywords/tags for playlists given tracks, indicating that the latent patterns learned from tracks in playlists are able to provide a good understanding of playlist titles and descriptions. The identified latent patterns are further leveraged to improve model performance on the task of predicting missing tracks given playlist titles and descriptions. Experimental results show that the proposed models yield improvements to the task, especially when playlist descriptions are provided as model input in addition to titles. The main contributions of this work include (1) providing a better solution to dealing with ``cold-start'' playlists in music recommender systems, and (2) proposing an effective approach to automatically generating descriptive keywords/tags for playlists using track sequences