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4. Piano roll representation of a MIDI file We visualize the first four measures of the Violin I part from Beethoven's "Fidelio" overture by displaying the MIDI events as a piano roll. The bars indicate note events of a specified duration.
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With the tremendously growing impact of digital technology, the ways of accessing music crucially changed. Nowadays, streaming services, download platforms, and private archives provide a large amount of music recordings to listeners. In the area of Music Information Retrieval, researchers are developing automatic methods for organizing and browsin...
Citations
... However, such features are less suitable for discriminating sub-genres or historical periods within Western classical music-consider, e. g., the co-existence of solo piano music composed over several centuries [9]. To address this challenge, harmonic features have shown promising results [10][11][12][13]. Yet, existing harmonic audio features exhibit two main limitations. ...
... Fixed-size segments with equal duration are adopted at multiple time scales or resolutions to capture the piece's different harmonic or tonal dimensions. Following [10][11][12], we consider four time resolutions in our work: 100ms, 500ms, 10s, and global (i.e., the entire piece or excerpt under analysis). Smaller time scales (e.g., 100ms and 500ms) capture finer musical elements such as individual notes, intervals, and chords. ...
... For our experiments, we consider the Cross-Era 1 and Cross-Composer 2 datasets, which include 1600 and 1100 pieces, respectively. In detail, the Cross-Era dataset has 1 https://www.audiolabs-erlangen.de/resources/MIR/cross-era 2 https://www.audiolabs-erlangen.de/resources/MIR/cross-comp Table 3. Balanced subsets obtained from the Cross-Era and Cross-Composer datasets [12]. ...
The extraction of harmonic information from musical audio is fundamental for several music information retrieval tasks. In this paper, we propose novel harmonic audio features based on the perceptually-inspired tonal interval vector space, computed as the Fourier transform of chroma vectors. Our contribution includes mid-level features for musical dissonance, chromaticity, dyadicity, triadicity, diminished quality, diatonicity, and whole-toneness. Moreover, we quantify the perceptual relationship between short-and long-term harmonic structures, tonal dispersion, harmonic changes, and complexity. Beyond the computation on fixed-size windows, we propose a context-sensitive harmonic segmentation approach. We assess the robustness of the new harmonic features in style classification tasks regarding classical music periods and composers. Our results align with, slightly outperforming, existing features and suggest that other musical properties than those in state-of-the-art literature are partially captured. We discuss the features regarding their musical interpretation and compare the different feature groups regarding their effectiveness for discriminating classical music periods and composers.
... He has published several papers on computational analyses in classical music, which unlike Simonton do not focus on melodic originality or thematic fame. Weiß's original doctoral dissertation was on the topic of "Computational Methods for Tonality-Based Style Analysis" [19] and he has continued research in this field specifically looking at tonal complexity and tonality-based style analysis [18]. He and his colleges make up a majority of recent publications in classical music computational analyses [20]. ...
In this work, the researcher presents a novel approach to calculating melodic originality based on the research by Simonton (1994). This novel formula is then applied to a dataset of 428 classical music pieces from the Romantic period to analyze the relationship between melodic originality and thematic fame.
... There are several classification algorithms, both using supervised learning (SVM, K-nearest neighbors) and unsupervised learning (K-means clustering). Weiss (2017) and Weiss et al. (2018) apply several of these methods using audio features to characterise and then classify composers. ...
This article illustrates different information visualization techniques applied to a database of classical composers and visualizes both the macrocosm of the Common Practice Period and the microcosms of twentieth century classical music. It uses data on personal (composer-to-composer) musical influences to generate and analyze network graphs. Data on style influences and composers ‘ecological’ data are then combined to composer-to-composer musical influences to build a similarity/distance matrix, and a multidimensional scaling analysis is used to locate the relative position of composers on a map while preserving the pairwise distances. Finally, a support-vector machines algorithm is used to generate classification maps. This article falls into the realm of an experiment in music education, not musicology. The ultimate objective is to explore parts of the classical music heritage and stimulate interest in discovering composers. In an age offering either inculcation through lists of prescribed composers and compositions to explore, or music recommendation algorithms that automatically propose works to listen to next, the analysis illustrates an alternative path that might promote the active rather than passive discovery of composers and their music in a less restrictive way than inculcation through prescription.
... The basic frequency is described as the minimum frequency of a stationary rhythmic sound signal, that can be described as tonal sound. Tonality in audio is a system that arranges musical scale notes based on musical criteria (Weiß 2017;Demirel et al. 2018). The basic frequency is described as the minimum frequency of a stationary rhythmic sound signal, that can be described as tonal sound. ...
Audio signal processing is the most challenging field in the current era for an analysis of an audio signal. Audio signal classification (ASC) comprises of generating appropriate features from a sound and utilizing these features to distinguish the class the sound is most likely to fit. Based on the application’s classification domain, the characteristics extraction and classification/clustering algorithms used may be quite diverse. The paper provides the survey of the state-of art for understanding ASC’s general research scope, including different types of audio; representation of audio like acoustic, spectrogram; audio feature extraction techniques like physical, perceptual, static, dynamic; audio pattern matching approaches like pattern matching, acoustic phonetic, artificial intelligence; classification, and clustering techniques. The aim of this state-of-art paper is to produce a summary and guidelines for using the broadly used methods, to identify the challenges as well as future research directions of acoustic signal processing.
... There are several classification algorithms, both using supervised learning (SVM, Knearest neighbors) and unsupervised learning (K-means clustering). Weiss (2017) and Weiss et al. (2018) apply several methods using audio features to characterise and then classify composers. ...
This paper and its companion article (I. An Application of Network Graphs) illustrate different information visualization techniques applied to a database of classical composers. In the first paper we used data on 'personal' (composer-to-composer) musical influences to generate and analyze (social) network graphs. In this second article we derive a composers' similarity matrix on the basis of musical (personal and style) influences and ecological data (features of a composer). We then use the similarity indices to locate composers on multidimensional scaling maps, and use machine-learning classification algorithms (e.g., support-vector machines and K-nearest neighbors) to generate classification maps. Both the macrocosm of the Common Practice Period and the microcosms of 20 th century classical music are visualized. The ultimate objective is to enhance basic music education and stimulate interest in discovering composers by proposing graphs and maps tracking the interconnections of composers. In an age offering either inculcation through lists of prescribed composers and compositions to explore, or music recommendation algorithms that automatically propose works to listen to next, the two articles illustrate an alternative path that might promote the active rather than passive discovery of composers and their music in a less restrictive way than inculcation through prescription.
... There are several classification algorithms, both using supervised learning (SVM, K-Nearest Neighbors) and unsupervised learning (K-means clustering). Weiss (2017) and Weiss et al. (2018) apply several methods using audio features to characterise and then classify composers. ...
This paper illustrates different information visualization techniques (data visualization) applied to a classical composers' database. In particular we present composers network graphs, heat maps and multidimensional scaling maps (the latter two obtained from a composer distance matrix), composers' classification maps using support-vector machine and K-Nearest Neighbors algorithms, and dendrograms. All visualization techniques have been developed using Python programming and libraries. The ultimate objective is to enhance basic music education and interest in classical music by presenting information quickly and clearly, taking advantage of the human visual system's ability to see patterns and trends.
... ). Motivated by [23,24], we prefer the latter arrangement accounting for the similarity of fifth-related scales, which have six out of seven pitch classes in common. We set the diatonic scale corresponding to the piece's global key in the center of the visualization, with upper-fifth-related scales (more sharps) above and lower-fifth-related scales (more flats) below that center scale. ...
... [13][14][15][16][17][18][19][20][21], and a cadence passage (mm. [21][22][23][24][25][26], which ends after a G major scale on the single tone G without having modulated (see Figure 4). 2 From m. 27 (0:43) on, this is followed by the melodic motif in G minor mentioned above, which is repeated in D minor (m. 33) and continued towards A minor (m. ...
The computational analysis of music has traditionally seen a sharp divide between the "audio approach" relying on signal processing and the "symbolic approach" based on scores. Likewise, there has also been an unfortunate gap between any such computational endeavour and more traditional approaches as used in historical musicology. In this paper, we take a step towards ameliorating this situation through the application of a computational method for visualizing local key characteristics in audio recordings. We exploit these visualizations of diatonic scale content by discussing their musicological implications, being aware of methodological limitations as for the case of minor keys. As a proof of concept, we use this method for investigating differences between the traditional sonata-form model and selected Beethoven piano sonatas in the context of sonata theory from the end of the 18 th century. We consider this scenario as an example for a rewarding dialogue between computer science and historical musicology.
... The unit of study is typically the 'composition' and the analysis is to compare similarities/differences across audio signals from a series of compositions or from different segments of a same composition (self-similarity). This particular angle has been used among others, by Foote (1999), Pampalk (2006), and more recently by Weiss (2017) and Weiss et al. (2018) for classical music, and by Mauch et al. (2015) for popular music where they investigate the evolution of musical diversity and disparity and whether evolution has been gradual or punctuated. Context-based MIR is motivated by the fact that there are aspects not encoded in an audio signal or that cannot be extracted from it, but which are nevertheless important to human perception of music, for example, the cultural background of a composer. ...
... , where a, b, c, d are the count of composers in sets described in Footnote 4. Weiss et al. 2018) typically show that composers tend to cluster in ways that conform to our intuitions about stylistic traditions. In this paper we will compare the clustering performance of our context-based approach versus the content-based approach of Weiss (2017). Beyond grouping composers on a fixed number of clusters, we also investigate, as Weiss (2017), hierarchical clustering, which better highlights evolution and trends in classical music. ...
... Beyond grouping composers on a fixed number of clusters, we also investigate, as Weiss (2017), hierarchical clustering, which better highlights evolution and trends in classical music. Weiss (2017) does it for 70 composers and we will again compare results in the next section. Besides the 70 composers analysed in Weiss, our method permits to compute dendrograms for up to 500 composers representing seven centuries of classical music. ...
This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology.
... Typical tasks such as global key detection [2,3], local key detection [4,5], or chord recognition [6][7][8] relate to tonal structures on various temporal scales. Beyond these concrete analysis scenarios, tonal features showed success for classifying music recordings with respect to more abstract categories such as musical styles [9][10][11][12]. Concretely, features quantifying the presence of certain chord or interval types [9] or the tonal complexity [10] led to efficient and robust discrimination of the four eras Baroque, Classical, Romantic, and Modern (Figure 1c). ...
... Concretely, features quantifying the presence of certain chord or interval types [9] or the tonal complexity [10] led to efficient and robust discrimination of the four eras Baroque, Classical, Romantic, and Modern (Figure 1c). Beyond that, features describing transitions between consecutive chords (chord progression bigrams) turned out beneficial for this task [11]. From music theory [13], we know that such chord transitions bear style-relevant information (Section 2), which could be verified in a study based on audio recordings [14]. ...
... Using a naive approach, Weiss et al. [11,14] explicitly compute chord labels from audio recordings and derive chord transition features from the resulting label sequence by counting transitions of a particular type. To extract chord labels, they make use of a typical chord recognition pipeline [7] based on a suitable pitch class representation (chromagram) and Hidden Markov Models (HMMs) together with the Viterbi decoding algorithm [15]. ...
