Auto-tagging systems can enrich music audio by providing contextual information in the form of tag predictions. Such context is valuable to solve problems within the MIR field. The majority of re- cent auto-tagging research, however, only considers a fraction of tags from the full set of available annotations in the original datasets. Because of this restriction, potential relationships between tags remain unconsidered and tagging may be less rich. These relationships suggest alternative ways to establish an auto-tagging system. For instance, a few accurate annotations from experts can improve the richness and quality of the auto-tagging system by providing explicit context in addition to audio content features. In this work, we propose an adaptation to the auto-tagging task, semi auto-tagging, to demonstrate such potential. In our framework, tags are allowed as contextual input to the tag pre- diction system in addition to audio content information. The system then suggests additional relevant tags. We implement two models that fit within the framework: content-aware matrix factorization and graph convolutional networks. To see whether we can improve upon a traditional auto-tagger, we compare these models with a multilayer perceptron as a baseline. Experimental results show that semi auto-tagger models can predict relevant tags both in the absence and presence of an audio content feature, and can predict tags for previously unseen songs similarly to an audio content auto-tagger. Based on a tag embedding comparison, we find that semi auto-tagger models can better learn implicit relationships between tags with a similar text string representation when compared to the baseline.

Music Information Retrieval (MIR) is a field of research that focusses on extracting information from music related data. This includes the genre of music and the beats per minute (BPM) of a song. Pipelines that extract this information from music are called feature extractors. Essentia is a library for such feature extraction. Often, the audio codec and quality is not considered in research setups within the field of MIR, while this could have an influence on the results. Therefore the main research question is "How do different audio codecs and audio quality impact genre classification and beats per minute (BPM) recognition in Essentia?". To answer this, the genre has been narrowed down to rock and the chosen audio codecs are FLAC, MP3 LAME and OGG Voribs. In collaboration with Muziekweb, a Dutch music library that collects all music that has been released in The Netherlands, it was possible to gather music files in lossless format. To degrade the audio quality, classify songs and recognize BPM, python pipelines for codec conversion, rock genre classification and BPM recognition were created an ran on this data. It has been concluded that changes in audio codec and quality have an influence on genre classification and BPM recognition in Essentia. It has not been concluded which codec and quality is best to use in the field of MIR. Further research is needed to answer this.

Working with trustworthy classifier models is important to the field of music information retrieval. However studies have shown some of the classifier models may not be as trustworthy as they appear. In this paper, we examine three of such classifiers available in the Essentia toolkit that have been evaluated using cross-validation, and measure the accuracy of these genre classifiers using cross-collection methods. We define a methodology inspired by other research in information retrieval to compare the output of the classifiers to an independent set of ground truth annotations that were the result of collaboration between the users of Last.fm. The classifiers were evaluated on 341 songs from the Muziekweb collection, and the results show that the classifiers performed worse than their cross-validation results.

Beat detection is an important MIR research area. Due to its growing usage in multimedia applications, the need for systematic ways to evaluate beat detectors is growing too. This research tests RhythmExtractor2013, a pipeline offered by Essentia, an open-source music analysis library used in research and industry. The annotated test samples, taken from four open-source datasets - GTZAN, Ballroom, SMC MIREX and MDB Drums, had tempo transformations (uniform, randomized, incremental and decremental tempo changes) applied to them and put to test against the aforementioned extractor. F-measure was chosen to calculate the extractor’s accuracy. The results show, that the accuracy is affected mostly by the presence of steady rhythm and drums, but also by the window size during the result calculation process, with the worst scores appearing when the samples are slowed down.

The GTZAN dataset, a collection of 1000 songsspanning 10 genres, proposed by Tzanetakis hasbeen around for 20 years. In this time hundredsof researches and applications have included thisdatabase. However, there seem to be some seri-ous limitations to this dataset. There are dupli-cates, mislabellings, low audio recordings and nar-row representations of genres. This paper aimsto research the effects of both audio quality andthe content of this dataset on genre classification.A Support Vector Machine (SVM) has been usedto retrain and compare different versions of thedataset. Two experiments have been proposed inthe paper. In the first experiment, a comparison be-tween a lossless dataset of high audio quality andan mp3 version of that same dataset of a loweraudio quality have been investigated. The lowerquality dataset performed worse on the SVM clas-sifier of this size. The second experiment pro-posed a new metal dataset, based on a wider andmore balanced range of metal sub-genres. Thismetal dataset has replaced the original metal partof the GTZAN dataset. Some retrainings done thisway had a higher accuracy than the original, givingconfidence that representing a well-balanced genremight improve classification performance. Finally,it has been found that the original GTZAN classi-fier is inaccurate on audio samples outside of itsdataset, where the new retrainings done on losslessdatasets without much preprocessing seem to per-form substantially better. This last finding has notbeen verified systematically and asks for more ver-ification.

Audio fingerprinting is one of the standard solutions for music identification. The underlying technique is designed to be robust to signal degradation such that music can be identified despite its presence. One of the newly emerged applications of a possibly challenging nature is music identification in movies. This paper examines the audio fingerprinting framework Dejavu by evaluating its performance against an existing benchmark created for the context of music identification in movies. The results show that Dejavu’s performance matches the expectations derived from the implementation and previous testing, and can be reconfigured to improve the performance in terms of the benchmark.

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 songs in order to retrieve the original song. In this research, we aim to answer the question of how music in movies can be identified using an audio fingerprinting platform called Audioneex. Two configurable parameters in the framework have been varied, of which the configurations have been evaluated in terms of a benchmark that has been established on synthesized data. The best performing configuration found in the limits of the parameters has been compared to the base configuration of Audioneex. Based on the results, the selected configuration improved the performance of the Audioneex framework by 1 match for one matching algorithm and by 3 matches for the other available matching algorithm.

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 challenges, music identification of movies, in a systematic way using the open-source fingerprinting framework called Panako. First, clips containing music were extracted from movies and queried using the default settings for Panako. Then, movie soundtracks were modified by layering noise over them, or by time-stretching and pitch-shifting them, and the performance of Panako on these modified audio signals was evaluated using a benchmark. Finally, the best configurations from the previous step were again queried using actual movie clips. These tests showed that both the default configuration and the configurations that performed best on the synthesised data perform poorly in movie music identification. Less than 10\% of the clips were identified correctly. The limited scope of this research, combined with the results gathered, show that there should be further investigation into the suitability of Panako for movie music identification.

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 processed using mixing and mastering techniques. This paper evaluates the suitability of the audio fingerprinting framework `SoundFingerprinting' for the identification of music in movies. The framework is evaluated according to a benchmark established for this field. The framework was tested on actual movie data, noise-layered soundtracks, pitch-shifted soundtracks and tempo-changed soundtracks. The framework was unable to identify the music in actual movie data, thus directing the research to identify problematic areas specific to SoundFingerprinting. In identifying noise-layered soundtracks, the framework showed varying performance dependent on the dominant frequencies present in the noise sample. Furthermore, the framework showed to be robust to tempo-changes, whereas the framework was unable to identify pitch-shifted soundtracks. Based on this performance evaluation, SoundFingerprinting is ill-equipped for the task of music identification 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 determined in the benchmark and to see if the performance can be increased. The OLAF framework makes use of parameters, which are individually tweaked and benchmarked with synthesised data. These findings are then combined to find a set of parameters that is used to perform this same benchmark on real clips from movies. The found parameter setup slightly increased the performance, going from 1 true positive and 133 false negatives in the original setup to 5 true positives, 3 false positives and 126 false negatives.