Time-Frequency Analysis.

Sensorimotor synchronization (SMS) is the coordination of rhythmic movement with an external rhythm, ranging from finger tapping in time with a metronome to musical ensemble performance. An earlier review (Repp, 2005) covered tapping studies; two additional reviews (Repp, 2006a, b) focused on music performance and on rate limits of SMS, respectively. The present article supplements and extends these earlier reviews by surveying more recent research in what appears to be a burgeoning field. The article comprises four parts, dealing with (1) conventional tapping studies, (2) other forms of moving in synchrony with external rhythms (including dance and nonhuman animals’ synchronization abilities), (3) interpersonal synchronization (including musical ensemble performance), and (4) the neuroscience of SMS. It is evident that much new knowledge about SMS has been acquired in the last 7 years.

/pdf/sensorimotor-synchronization-a-review-of-recent-research-ymvlozhb1e.pdf

Sensorimotor synchronization: A review of recent research (2006–2012)

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects.

/pdf/automatic-music-transcription-challenges-and-future-2qs8dxdnju.pdf

Automatic music transcription: challenges and future directions

Ensemble musicians coordinate their actions with remarkable precision The ensemble cohesion that results is predicated upon group members sharing a common goal; a unified concept of the ideal sound The current chapter reviews research addressing three cognitive processes that enable individuals to realize such shared goals while engaged in musical joint action The first process is auditory imagery; specifically, anticipating one’s own sounds and the sounds produced by other performers The second process, prioritized integrative attention, involves dividing attention between one’s own actions (high priority) and those of others (lower priority) while monitoring the overall, integrated ensemble sound The third process relates to adaptive timing, ie, adjusting the timing of one’s movements in order to maintain synchrony in the face of tempo changes and other, often unpredictable, events The way in which these processes interact to determine ensemble coordination is discussed

/pdf/joint-action-in-music-performance-1rzkuh8951.pdf

Joint action in music performance

http://pkpublications.weebly.com/uploads/1/1/8/3/11835433/reppkeller_hms2008.pdf

Sensorimotor synchronization with adaptively timed sequences

We investigate the effects of different levels of delay (or latency) on the coordination, pace and timing regularity of musicians who are in remote locationsâ€”a situation encountered in an interactive network performance. Two pairs of musicians performed two Mozart duets while isolated visually and connected through microphones and headphones. Different levels of latency (0, 20, 40, 50, 80, 100, 120, 150, and 200 ms) were introduced into the performing environment (musicians heard themselves in real time and only the other part delayed); the musicians performed the duets under these conditions and rated their musicality and level of interactivity. Although the musicians chose different strategies to handle the latency, which resulted in different levels of success in maintaining coordination, pacing and regularity, both duets were strongly affected by latency at and above 100 ms. At these levels, the musicians rated the performances as neither musical nor interactive, and they reported that they played as individuals and listened less and less to one another.

Effect of Network Latency on Interactive Musical Performance

This paper answers suspicions that Schenkerian theory is powerful only because it is somehow riven with circular assumptions and ad hoc inferences by discovering and testing boundaries to the applicability of the theory. Our $IV/I V Hypothesis is based on Schenkerian theory's predictions that if IV/, V sonorities appear in a tonal context, then they are always indirectly related to the tonic; thus, whenever $IV/I V Stufen are interpreted as being directly related to the tonic or are not explainable in a convincing way as indirectly related, either the analysis is incorrect or the work must be deemed on-tonal. The direct analytical implications of the hypothesis provide us with a well-defined boundary to tonal practice under a Schenkerian interpretation. a number of pi ces containing apparent modulations to #IV I V. In other words, our knowledge of Schenkerian theory ormed our understa ding of the music at er than vice sa-it helped us to explain why we m ght interpret pases one way and not another. We b lieve t whatever gers there may be in making analysi subservient to the, there is an equal danger in neglec ing the formal propies of music theories. The moral is at theory nd analysis st always be in a cons ant dialogu : o e cannot grow propl withou the other. This content downloaded from 207.46.13.48 on Wed, 12 Oct 2016 05:45:53 UTC All use subject to http://about.jstor.org/terms

The ♯IV(♭ V) Hypothesis: Testing the Limits of Schenker's Theory of Tonality

Automatic Music Production System Employing Probabilistic Expert Systems

Each of the papers in this issue addresses how elements of George Perle’s compositional system and analytical methodology intersect with features of Klumpenhouwer networks (K-nets), drawing on Perle’s own communication on the subject as well as his writings.1 This commentary is intended both to clarify Perle’s position and to provide additional context from his writings. Lewin and Lambert directly refer to Perle’s materials. Lewin compares K-nets and “Perle cycles” (Perle calls them cyclic sets) as models for compositional events, and Lambert describes his K-net families in terms of Perle’s cyclic sets.2 Perle was initially in uenced by Berg’s Lyric Suite, where the row is comprised of two interlocking inversionally-related interval5 or -7 cycles and thus is comprised of adjacent dyads from two sums, 9 and 4, as shown in Example 1, where sums of 9 are bracketed on top, and those of 4 on bottom. Perle calls such a construct a “cyclic set.” A cyclic set is identi ed by its interval cycles and its sums; the cyclic interval is the difference of the sums (9– 4 = 5 or 4–9 = 7; either 5 or 7 may be used). Each imbricated trichord in the set is comprised of a middle “axis note” with surrounding “neighbor notes.” The trichord is the smallest unit that can identify a cyclic set, as it contains the two sums and the cyclic interval. In K-net terms, these trichords are all related by strong isography, with the same con guration of Ts and Is.3 For instance, {8,1,3}, {6,A,B} and {4,0,9} can all be interpreted as T5/7, I9/I4 K-nets. The given row-form of P5 (rows in Example 1 are rotated by one hexachord to create continuous cyclic sets) shares one sum with related forms at sum 9 (row-form I4), and sum 4 (row-form IB). These three rows vertically create the same group of K-nets related by strong isography when aligned. Cyclic sets that share one sum, such as the sums-9/2 and -9/4 sets, are called “cognate sets” by Perle. Cyclic sets are part of a “cyclic set complex” that includes all alignments of the two interlocking interval cycles. Example 2(a) reproduces Lewin’s Example 1.1, with T1 and TB cycles in an offset alignment and sums 6,7 found on the diagonals. In later examples, Lewin maintains this offset arrangement, showing trichords as triangles between the two sets, and does not represent them in one line, as Perle does in his cyclic sets. This example is rewritten as a cyclic set of sums 6/7 in Example 2(b). It is placed within a cyclic set complex, where the sets are based on interval cycles 1/B. The interval-B cycle in boldface shifts one to the right in 1See the citation list for pertinent material. 2Lewin writes that Perle suggests that K-nets “can best be viewed” in terms of cycles, but Perle only notes that they “may be de ned and ef ciently and economically represented in this way.” Perle 1993, 300. 3Tracing this type of row would open a twelve-tone component to K-nets.

https://www.jstor.org/stable/pdfplus/10.1525/mts.2002.24.2.246.pdf

Perle's Cyclic Sets and Klumpenhouwer Networks: A Response

In this paper we propose a real-time signal processing framework for musical audio that 1) aligns the audio with an existing music score or creates a musical score by automated music transcription algorithms; and 2) obtains the expressive feature descriptors of music performance by comparing the score with the audio. Real-time audio segmentation algorithms are implemented to identify the onset points of music notes in the incoming audio stream. The score related features and musical expressive features are extracted based on these segmentation results. In a realtime setting, these audio segmentation and feature extraction operations have to be accomplished at (or shortly after) the note onset points, when an incomplete length of audio signal is captured. To satisfy real-time processing requirements while maintaining feature accuracy, our proposed framework combines the processing stages of prediction, estimation, and updating in both audio segmentation and feature extraction algorithms in an integrated refinement process. The proposed framework is implemented in a MATLAB real-time signal processing framework.

/pdf/a-real-time-signal-processing-framework-of-musical-4m01i7gakc.pdf

Dave Headlam

Papers

Effect of Network Latency on Interactive Musical Performance

The ♯IV(♭ V) Hypothesis: Testing the Limits of Schenker's Theory of Tonality

Automatic Music Production System Employing Probabilistic Expert Systems

Perle's Cyclic Sets and Klumpenhouwer Networks: A Response

A real-time signal processing framework of musical expressive feature extraction using matlab