Your Turn To Roll: Exploring gaps in group recommendation research

In group recommendation, a key question is how preferences from individuals should be obtained and then aggregated into a group outcome. Collecting individual preferences can be done through implicit or explicit means, but there is insufficient research available on what option is optimal. For comparing different possible aggregation strategies,...

Understanding Data Scientists’ use of Explainability and Interpretability tools

Machine learning techniques are being used increasingly in high-risk domains such as healthcare, law, and finance. Misclassifications or mispredictions in areas like these can have serious consequences. Therefore, it is crucial to have high performing models that can make as minimal errors as possible. However, in some cases, it is not enough to...

Recommender Systems with Evolutionary Algorithms: Many­-Objective Optimization for Large­-Scale Music Recommendation: A demonstration showing the reliability of serving users recommendations with trade-­off for large music collections, by leveraging diverse Recommender Systems and Evolutionary Algorithms

Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a...

Assessing the Suitability of Panako for Music Identification in Movies

Audio fingerprinting is a technique that allows for fast identification of music. Research concerning this technique first emerged around the 2000s and has lead to several applications, like Shazam. More recently, developments in this area have slowed down, even though there are still new challenges emerging. This paper investigates one of these...

Analysing the performance of the OLAF framework in the context of identifying music in movies

This paper presents the findings of a benchmark performed on the audio fingerprinting framework OLAF in the context of movie music. The goal is to find a music identification framework suitable for automatically identifying a song from a movie clip. This research aims to find how well OLAF performs in this context with regard to the criteria...

Evaluating the Suitability of SoundFingerprinting for Music Identification in Movies

Audio fingerprinting has shown to be an effective approach to music identification, having properties robust to noise and signal degradations. A field in which audio fingerprinting has not been evaluated yet is music identification in movies. In movies, music is often accompanied with background noise, sound effects and dialogue, and further...

Indexing Music in Movies Using Audio Fingerprinting: An Audioneex Study

Music indexing, the practice of identifying songs contained in an audio sample, is an approach that is widely used. As an underlying technique, "audio fingerprinting" can be used. In this technique, an audio sample is converted to a fingerprint; a smaller representation of the audio. This fingerprint is compared to a database of fingerprinted...

More than a feeling?: Reliability and robustness of high-level music classifiers

High-level music classification tasks such as automatic music mood annotation impose several challenges, both from a psychological and a machine learning point of view. Ground truth labels for these tasks at hand are hard to define due to the abstract and aesthetic nature of the data, being largely dependent on human psychology and perception....

Example and Feature importance-based Explanations for Black-box Machine Learning Models

Machine Learning (ML) is a rapidly growing field. There has been a surge of complex black-box models with high performance. On the other hand, the application of these models especially in high-risk domains is more stagnant due to lack of transparency and trust in these black-box models. There is a disconnect between the black-box character of...

Image colourization by neural style

Image colourization is the task of providing colour to grayscale images or videos. Image colourization can be valuable to the entertainment industry and of cultural interest. Current methods colourize images by either providing an interface for easier colourization by a person or by using an automatic method which nowadays are often driven by a...

Probabilistic Models for Personalized Faceted Search

Extracting information from collections of musical performances using Dynamic Time Warping

When performing a piece of music, there are certain ways in which performers turn the score into their own musical interpretation of it. The difference between what is written and what is played can be substantial, and can vary between performers and even individual performances. In this thesis we examine what information can be gained from...

Plugify: Online booking platform for live music