The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure

The behavior of users of music streaming services is investigated from the point of view of the temporal dimension of individual songs; specifically, the main object of the analysis is the point in time within a song at which users stop listening and start streaming another song ("skip"). The main contribution of this study is the ascertainment of a correlation between the distribution in time of skipping events and the musical structure of songs. It is also shown that such distribution is not only specific to the individual songs, but also independent of the cohort of users and, under stationary conditions, date of observation. Finally, user behavioral data is used to train a predictor of the musical structure of a song solely from its acoustic content; it is shown that the use of such data, available in large quantities to music streaming services, yields significant improvements in accuracy over the customary fashion of training this class of algorithms, in which only smaller amounts of hand-labeled data are available

Structural Segmentation using Set Accented Tones

An approach which efficiently segments Irish Traditional Music into its constituent structural segments is presented. The complexity of the segmentation process is greatly increased due to melodic variation existent within this music type. In order to deal with these variations, a novel method using ‘set accented tones’ is introduced. The premise is that these tones are less susceptible to variation than all other tones. Thus, the location of the accented tones is estimated and pitch information is extracted at these specific locations. Following this, a vector containing the pitch values is used to extract similar patterns using heuristics specific to Irish Traditional Music. The robustness of the approach is evaluated using a set of commercially available Irish Traditional recordings

Segmentation & the Jobs-to-be-done theory: A Conceptual Approach to Explaining Product Failure

Based on Christensen et al.’s research the jobs-to-be-done theory tends to hold that (market) segmentation is a theory (2004, 2003, 2003). The criticism expressed is that companies frequently allocate their market segments close to attributes, which are easy to measure and just observe consumers’ behaviour for developing new products. There exists the phenomenon that the vast majority of new products fail within a short period of time after market entry. The jobs-to-be-done theory supports that it is more important to align R&D alongside jobs consumers need to get done, jobs, which facilitate their lives and for which they searched a solution historically. The proposition the jobs-to-be-done theory offers is the identification of such jobs needing solutions, which may lead to the creation of new markets or to the extension of existing ones, which do not provide good enough products. Scholars and academics put much emphasis on the process of segmentation – targeting – positioning, as an important tool to focus organisational resources and capabilities for the achievement of sustainable positioning in a challenging market environment. Therefore, this specific theory challenges what marketing theory considers as an important strategy for market success. The proposition is that both approaches established STP and the approach by the jobs-to-be-done theory need to be well considered within strategic organisational decision making, especially for R&D and product strategy. While the traditional STP-strategy seems salient within incremental product novelties, the jobs-to-be-done theory is suggested to offer assistance for more radical product developments. This way, organisations may find themselves in a dilemma to understand, where a borderline between incremental and more radical developments may be drawn, where it will be advantageous to rely on measuring consumer behaviour by classical market research and data and at which point it will be more promising to follow the propositions of the jobs-to-be-done theory for developing successful new products. The proposition of this paper is that suggestions established by scholars’ for a sound segmentation strategy need to be contrasted with the jobs-to-be-done theory in the understanding that there are market needs for incremental improvements and parallel to these, different markets are expecting more radical solutions to get jobs done, for which existing products are not good enough. The paper’s conclusion will result in propositions of framing both these macro markets and contrasting them against each other. Key Words: Jobs-to-be-done theory, segmentation, STP-strategy, new product developmen

Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings

Structure perception is a fundamental aspect of music cognition in humans. Historically, the hierarchical organization of music into structures served as a narrative device for conveying meaning, creating expectancy, and evoking emotions in the listener. Thereby, musical structures play an essential role in music composition, as they shape the musical discourse through which the composer organises his ideas. In this paper, we present a novel music segmentation method, pitchclass2vec, based on symbolic chord annotations, which are embedded into continuous vector representations using both natural language processing techniques and custom-made encodings. Our algorithm is based on long-short term memory (LSTM) neural network and outperforms the state-of-the-art techniques based on symbolic chord annotations in the field

Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features

The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of structural boundaries of the music pieces. This structural boundary analysis has recently been studied with unsupervised methods and \textit{end-to-end} techniques such as Convolutional Neural Networks (CNN) using Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs and trained with human annotations. Several studies have been published divided into unsupervised and \textit{end-to-end} methods in which pre-processing is done in different ways, using different distance metrics and audio characteristics, so a generalized pre-processing method to compute model inputs is missing. The objective of this work is to establish a general method of pre-processing these inputs by comparing the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN in order to establish the most efficient way to extract the limits of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies we obtain a measurement accuracy $F_1$ of 0.411 that outperforms the current one obtained under the same conditions

Interaction features for prediction of perceptual segmentation:Effects of musicianship and experimental task

As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians’ segmentation yielded lower prediction rates, and involved more features for prediction, particularly more interaction features; also non-musicians required a larger time shift for optimal segmentation modelling. Prediction of the annotation task exhibited higher rates, and involved more musical features than for the real-time task; in addition, the real-time task required time shifting of the segmentation data for its optimal modelling. We also found that annotation task models that were weighted according to boundary strength ratings exhibited improvements in segmentation prediction rates and involved more interaction features. In sum, musical training and experimental task seem to have an impact on prediction rates and on musical features involved in novelty-based segmentation models. Musical training is associated with higher presence of schematic knowledge, attention to more dimensions of musical change and more levels of the structural hierarchy, and higher speed of musical structure processing. Real-time segmentation is linked with higher response delays, less levels of structural hierarchy attended and higher data noisiness than annotation segmentation. In addition, boundary strength weighting of density was associated with more emphasis given to stark musical changes and to clearer representation of a hierarchy involving high-dimensional musical changes.peerReviewe

Explaining Listener Differences in the Perception of Musical Structure

PhDState-of-the-art models for the perception of grouping structure in music do not attempt to account for disagreements among listeners. But understanding these disagreements, sometimes regarded as noise in psychological studies, may be essential to fully understanding how listeners perceive grouping structure. Over the course of four studies in different disciplines, this thesis develops and presents evidence to support the hypothesis that attention is a key factor in accounting for listeners' perceptions of boundaries and groupings, and hence a key to explaining their disagreements. First, we conduct a case study of the disagreements between two listeners. By studying the justi cations each listener gave for their analyses, we argue that the disagreements arose directly from differences in attention, and indirectly from differences in information, expectation, and ontological commitments made in the opening moments. Second, in a large-scale corpus study, we study the extent to which acoustic novelty can account for the boundary perceptions of listeners. The results indicate that novelty is correlated with boundary salience, but that novelty is a necessary but not su cient condition for being perceived as a boundary. Third, we develop an algorithm that optimally reconstructs a listener's analysis in terms of the patterns of similarity within a piece of music. We demonstrate how the output can identify good justifications for an analysis and account for disagreements between two analyses. Finally, having introduced and developed the hypothesis that disagreements between listeners may be attributable to differences in attention, we test the hypothesis in a sequence of experiments. We find that by manipulating the attention of participants, we are able to influence the groupings and boundaries they find most salient. From the sum of this research, we conclude that a listener's attention is a crucial factor affecting how listeners perceive the grouping structure of music.Social Sciences and Humanities Research Council; a PhD studentship from Queen Mary University of London; a Provost's Ph.D. Fellowship from the University of Southern California. This material is also based in part on work supported by the National Science Foundation under Grant No. 0347988

A Planning-based Approach for Music Composition

. Automatic music composition is a fascinating field within computational creativity. While different Artificial Intelligence techniques have been used for tackling this task, Planning – an approach for solving complex combinatorial problems which can count on a large number of high-performance systems and an expressive language for describing problems – has never been exploited. In this paper, we propose two different techniques that rely on automated planning for generating musical structures. The structures are then filled from the bottom with “raw” musical materials, and turned into melodies. Music experts evaluated the creative output of the system, acknowledging an overall human-enjoyable trait of the melodies produced, which showed a solid hierarchical structure and a strong musical directionality. The techniques proposed not only have high relevance for the musical domain, but also suggest unexplored ways of using planning for dealing with non-deterministic creative domains