Musical complexity and cross-modal selective attention: the effects of irrelevant auditory distractors on a concurrent reaction-time task

University of the Witwatersrand MA Research Psychology 2014This study investigates the effects of complex music on concurrent task performance in a response-competition paradigm. Past research in this domain have produced disparate results, ranging from deleterious to facilitative effects. However, such research has failed to account for schematic expectancy violation in its operationalization of melodic complexity. Competing models of cross-modal cognition were therefore evaluated using atonal and tonal musical compositions in a quasi-experimental research design, with response times in the attentional network task (ANT) used to infer whether music had a facilitative or distracting effect on task performance. Participants were recruited from the University of the Witwatersrand’s School of Human and Community Development. The computer-based attentional network task (ANT) was administered using the E-prime software, while participants were concurrently exposed to music. Repeated-measure ANOVAs were run to determine whether differences in means attained were significant. The results were consistent with Hockey’s (1997) compensatory control model, which predicted faster reaction times during concurrent exposure to complex music due to the activation of a top-down cognitive mechanism which allots greater working memory resources to the primary task. This increase in working memory resources should have led to reduced involuntary attentional switching, thus focused selective attention and enhanced task performance. While the model also predicted a performance-cost tradeoff in the form of physiological distress, self-reported measures of affective and physiological states yielded no statistically significant differences between music conditions. These findings are discussed against a backdrop of past research findings, and recommendations for future studies made accordingly

Automated Rhythmic Transformation of Drum Recordings

Within the creative industries, music information retrieval techniques are now being applied in a variety of music creation and production applications. Audio artists incorporate techniques from music informatics and machine learning (e.g., beat and metre detection) for generative content creation and manipulation systems within the music production setting. Here musicians, desiring a certain sound or aesthetic influenced by the style of artists they admire, may change or replace the rhythmic pattern and sound characteristics (i.e., timbre) of drums in their recordings with those from an idealised recording (e.g., in processes of redrumming and mashup creation). Automated transformation systems for rhythm and timbre can be powerful tools for music producers, allowing them to quickly and easily adjust the different elements of a drum recording to fit the overall style of a song. The aim of this thesis is to develop systems for automated transformation of rhythmic patterns of drum recordings using a subset of techniques from deep learning called deep generative models (DGM) for neural audio synthesis. DGMs such as autoencoders and generative adversarial networks have been shown to be effective for transforming musical signals in a variety of genres as well as for learning the underlying structure of datasets for generation of new audio examples. To this end, modular deep learning-based systems are presented in this thesis with evaluations which measure the extent of the rhythmic modifications generated by different modes of transformation, which include audio style transfer, drum translation and latent space manipulation. The evaluation results underscore both the strengths and constraints of DGMs for transformation of rhythmic patterns as well as neural synthesis of drum sounds within a variety of musical genres. New audio style transfer (AST) functions were specifically designed for mashup-oriented drum recording transformation. The designed loss objectives lowered the computational demands of the AST algorithm and offered rhythmic transformation capabilities which adhere to a larger rhythmic structure of the input to generate music that is both creative and realistic. To extend the transformation possibilities of DGMs, systems based on adversarial autoencoders (AAE) were proposed for drum translation and continuous rhythmic transformation of bar-length patterns. The evaluations which investigated the lower dimensional representations of the latent space of the proposed system based on AAEs with a Gaussian mixture prior (AAE-GM) highlighted the importance of the structure of the disentangled latent distributions of AAE-GM. Furthermore, the proposed system demonstrated improved performance, as evidenced by higher reconstruction metrics, when compared to traditional autoencoder models. This implies that the system can more accurately recreate complex drum sounds, ensuring that the produced rhythmic transformation maintains richness of the source material. For music producers, this means heightened fidelity in drum synthesis and the potential for more expressive and varied drum tracks, enhancing the creativity in music production. This work also enhances neural drum synthesis by introducing a new, diverse dataset of kick, snare, and hi-hat drum samples, along with multiple drum loop datasets for model training and evaluation. Overall, the work in this thesis increased the profile of the field and hopefully will attract more attention and resources to the area, which will help drive future research and development of neural rhythmic transformation systems

Hungarian Gypsy Style in the Lisztian Spirit: Georges Cziffra's Two Transcriptions of Brahms' Fifth Hungarian Dance

Georges (György) Cziffra (1921-1994), the piano virtuoso of Hungarian gypsy origin, developed bewildering skills of improvisation and technical brilliance at the piano. His deep fascination with Franz Liszt's music influenced his playing style and musical spirit, and his critics, highly speaking of his Romantic pianism and especially emphasizing his virtuosity, often held him as one of the most outstanding Liszt performers of our age. Cziffra's love for Hungarian themes moved him to perform and record numerous improvisations based on Magyar melodies. Later in his life he preserved many of his own extemporized adaptations in notation, including his transcriptions of fifteen of the Hungarian Dances by Johannes Brahms. The focus of the paper is on Georges Cziffra's two piano transcriptions (1957 and 1982-83) of Brahms' Fifth Hungarian Dance (1868). The examination and analysis of these two versions in comparison with the original Hungarian sources and Brahms' own arrangement reveal Cziffra's style as a virtuoso improviser and transcriber. Examples from Liszt's Hungarian Rhapsodies serve to identify the Lisztian features in Cziffra's transcriptions. The characteristic elements of the Hungarian gypsy musicians' improvisatory style, which influenced and inspired both Liszt and Brahms, as well as Cziffra, receive particular attention. Chapter 2 offers a brief history of the Hungarian gypsy musicians, depicts their life and social status in the nineteenth and twentieth centuries, examines the most characteristic elements of their performance technique, and portrays their musical-stylistic influence on Hungarian music, the stylistic conglomeration of which became the foundation for the renowned style hongrois. Chapter 3 examines the acquaintance of Liszt and Brahms with Hungarian music in the gypsy style and reviews basic information about Liszt's Hungarian Rhapsodies and Brahms' Hungarian Dances. Chapter 4 offers biographical information about György Cziffra and investigates his association with the music of Liszt, Brahms, and the Hungarian gypsy musicians. Cziffra's musical and transcribing style and a general discussion of his Transcriptions: Grandes Etudes de Concert I (Frankfurt: Peters, 1995) are also included here. Chapter 5 consists of information about the sources of the popular themes that Brahms used for the Hungarian Dances. Then the focus of this chapter is on the evolution of the Fifth Hungarian Dance from its sources through Brahms to the transcriptions of Cziffra, including the examination of Cziffra's 1957 transcription in comparison to the 1982-83 version. Selected examples of Liszt's Hungarian Rhapsodies are provided to support the identification of Lisztian features in Cziffra's work. The detection of the characteristic elements of the Hungarian gypsies' improvisatory style will receive particular attention

The Mozart effect on the episodic memory of healthy adults is null, but low-functioning older adults may be an exception

Literature on the effects of passive music listening on cognitive performance is mixed, showing negative, null or positive results depending on cognitive domain, age group, temporal relation between music and task (background music vs. music before task, the latter known as Mozart effect), or listener-dependent variables such as musical preference. Positive effects of background music on the two components of episodic memory – item and source memory - for verbal materials seem robust and age-independent, and thus deserve further attention. In the current study, we investigated two potential enhancers of music effects on episodic memory: stopping music before task performance (Mozart effect) to eliminate music-related distraction and using preferred music to maximize reward. We ran a main study on a sample of 51 healthy younger adults, along with a pilot study with 12 older adults, divided into low- vs. high functioning according to cognitive performance in a screening test. Against our expectations, Bayesian analyses showed strong evidence that music had no advantage over silence or environmental sounds in younger adults. Preferred music had no advantage either, consistent with the possibility that music-related reward had no impact on episodic memory. Among older adults, low- but not high-functioning participants’ item memory was improved by music – especially by non-preferred music - compared to silence. Our findings suggest that, in healthy adults, prior-to-task music may be less effective than background music in episodic memory enhancement despite decreased distraction, possibly because reward becomes irrelevant when music is stopped before the task begins. Our pilot findings on older adults raise the hypothesis that low-functioning older participants relate to prior-to-task auditory stimulation in deviant ways when it comes to episodic memory enhancement

Towards the Automatic Analysis of Metric Modulations

PhDThe metrical structure is a fundamental aspect of music, yet its automatic analysis from audio recordings remains one of the great challenges of Music Information Retrieval (MIR) research. This thesis is concerned with addressing the automatic analysis of changes of metrical structure over time, i.e. metric modulations. The evaluation of automatic musical analysis methods is a critical element of the MIR research and is typically performed by comparing the machine-generated estimates with human expert annotations, which are used as a proxy for ground truth. We present here two new datasets of annotations for the evaluation of metrical structure and metric modulation estimation systems. Multiple annotations allowed for the assessment of inter-annotator (dis)agreement, thereby allowing for an evaluation of the reference annotations used to evaluate the automatic systems. The rhythmogram has been identified in previous research as a feature capable of capturing characteristics of rhythmic content of a music recording. We present here a direct evaluation of its ability to characterise the metrical structure and as a result we propose a method to explicitly extract metrical structure descriptors from it. Despite generally good and increasing performance, such rhythm features extraction systems occasionally fail. When unpredictable, the failures are a barrier to usability and development of trust in MIR systems. In a bid to address this issue, we then propose a method to estimate the reliability of rhythm features extraction. Finally, we propose a two-fold method to automatically analyse metric modulations from audio recordings. On the one hand, we propose a method to detect metrical structure changes from the rhythmogram feature in an unsupervised fashion. On the other hand, we propose a metric modulations taxonomy rooted in music theory that relies on metrical structure descriptors that can be automatically estimated. Bringing these elements together lays the ground for the automatic production of a musicological interpretation of metric modulations.EPSRC award 1325200 and Omnifone Ltd