Audio Tempo Research Articles

Using cognitive mapping to analyze the aesthetic value of music education in colleges and universities

Abstract In this paper, firstly, on the basis of cognitive mapping, music data are processed in four directions, namely, sound spectrum extraction, audio superposition, audio tempo, and tone intensity adjustment, and then audio sequences are sliced and diced, so that the cognitive mapping can be more focused on capturing localized useful information. Then, the BERT model is used to encode the music sequences so that the output music sequences already contain aesthetic and emotional features and the cognitive mapping-based music emotion analysis model is constructed according to the music emotion learning layer, the emotion feature aggregation layer, and the full connectivity layer, and analyzes the aesthetic value of music education in colleges and universities. According to the results, the BERT model had a difference of 0.13% on the MED dataset and 0.03% on the MTD dataset. On the mean value of the aesthetic value of traditional music, introverted personality (3.61) was higher than extroverted personality (3.52). This study has a positive effect on the cultivation of aesthetic value in music education in colleges and universities and proposes effective ways to enhance aesthetic value in college and university music teaching.

THE USE OF AUDIO TRIMMER AS AN ONLINE APPLICATION TO IMPROVE STUDENTS' ABILITY IN ENGLISH LISTENING CLASS

This study focuses on the use of an online application-based Audio Trimmer in learning Listening in the English study program. The aim is to find out how the lecturer applies this Audio Trimmer application to Listening class and to find out the level of student satisfaction with the use of Audio Trimmer in Listening learning. Audio Trimmer is an online application that can adjust the audio tempo. So, no matter how difficult listening is, it will be resolved by using this application. Audio Trimmer has indeed been used as a learning medium in the Listening course for two semesters and there are indicators of student satisfaction with the use of the application. Therefore, this research is qualitatively designed with a case study approach with the research subject being all students from two listening classes, namely Extensive Listening and Academic Listening, and the data collection was obtained from student questionnaires in the form of a Likert scale questionnaire in the form of ten statements, class observation, and documentation of student achievements in listening courses. Then the research analysis includes data reduction, data display, and data verification. There are two findings from this study. First, the audio trimmer is used in listening courses at the whilst-activity and post-activity stages. Second, the student's perception shows that the audio trimmer is very supportive of listening learning. In conclusion, the audio trimmer in learning listening is very useful and of course highly recommended for English learners who will listen to English audio. Keywords: Audio Trimmer, online application, Listening

Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation

We present a method for tempo estimation from audio recordings based on signal processing and peak tracking, and not depending on training on ground-truth data. First, an accentuation curve, emphasizing the temporal location and accentuation of notes, is based on a detection of bursts of energy localized in time and frequency. This enables the detection of notes in dense polyphonic texture, while ignoring spectral fluctuation produced by vibrato and tremolo. Periodicities in the accentuation curve are detected using an improved version of autocorrelation function. Hierarchical metrical structures, composed of a large set of periodicities in pairwise harmonic relationships, are tracked over time. In this way, the metrical structure can be tracked even if the rhythmical emphasis switches from one metrical level to another. This approach, compared to all the other participants to the Music Information Retrieval Evaluation eXchange (MIREX) Audio Tempo Extraction competition from 2006 to 2018, is the third best one among those that can track tempo variations. While the two best methods are based on machine learning, our method suggests a way to track tempo founded on signal processing and heuristics-based peak tracking. Moreover, the approach offers for the first time a detailed representation of the dynamic evolution of the metrical structure. The method is integrated into MIRtoolbox, a Matlab toolbox freely available.

Virtual Reality and Musculoskeletal Pain: Manipulating Sensory Cues to Improve Motor Performance During Walking

Musculoskeletal pain (MSP) is the most expensive nonmalignant health problem and the most common reason for activity limitation. Treatment approaches to improve movement without aggravating pain are urgently needed. Virtual reality (VR) can decrease acute pain, as well as influence movement speed. It is not clear whether VR can improve movement speed in individuals with MSP without aggravating pain. This study investigated the extent to which different audio and optic flow cues in a VR environment influenced walking speed in people with and without MSP. A total of 36 subjects participated, 19 with MSP and 17 controls. All walked on a motorized self-paced treadmill interfaced with a three-dimensional virtual walkway. The audio tempo was scaled (75%, 100%, and 125%) from baseline cadence, and optic flow was either absent, or scaled to 50% or 100% of preferred walking speed. Gait speed was measured during each condition, and pain was measured before and after the experiment. Repeated measures analysis of variance showed that audio tempo above baseline cadence significantly increased walking speed in both groups, F(3, 99)=10.41, p<0.001. Walking speed increases of more than 25% occurred in both groups in the 125% audio tempo condition, without any significant increase in pain. There was also a trend toward increased walking speeds with the use of optic flow, but the results in this study did not achieve significance at the p<0.05 level, F(2, 66)=2.01, p=0.14. Further research is needed to establish the generalizability of increasing movement speed across different physical performance tasks in VR.

Sounding better: fast audio cues increase walk speed in treadmill-mediated virtual rehabilitation environments.

Music or sound effects are often used to enhance Virtual Environments, but it is not known how this audio may influence gait speed. This study investigated the influence of audio cue tempo on treadmill walking with and without visual flow. The walking speeds of 11 individuals were recorded during exposure to a range of audio cue rates. There was a significant effect of audio tempo without visual flow, with a 16% increase in walk speed with faster audio cue tempos. Audio with visual flow resulted in a smaller but still significant increase in walking speed (8%). The results suggest that the inclusion of faster rate audio cues may be of benefit in improving walk speed in virtual rehabilitation.

An experimental comparison of audio tempo induction algorithms

We report on the tempo induction contest organized during the International Conference on Music Information Retrieval (ISMIR 2004) held at the University Pompeu Fabra in Barcelona, Spain, in October 2004. The goal of this contest was to evaluate some state-of-the-art algorithms in the task of inducing the basic tempo (as a scalar, in beats per minute) from musical audio signals. To our knowledge, this is the first published large scale cross-validation of audio tempo induction algorithms. Participants were invited to submit algorithms to the contest organizer, in one of several allowed formats. No training data was provided. A total of 12 entries (representing the work of seven research teams) were evaluated, 11 of which are reported in this document. Results on the test set of 3199 instances were returned to the participants before they were made public. Anssi Klapuri's algorithm won the contest. This evaluation shows that tempo induction algorithms can reach over 80% accuracy for music with a constant tempo, if we do not insist on finding a specific metrical level. After the competition, the algorithms and results were analyzed in order to discover general lessons for the future development of tempo induction systems. One conclusion is that robust tempo induction entails the processing of frame features rather than that of onset lists. Further, we propose a new "redundant" approach to tempo induction, inspired by knowledge of human perceptual mechanisms, which combines multiple simpler methods using a voting mechanism. Machine emulation of human tempo induction is still an open issue. Many avenues for future work in audio tempo tracking are highlighted, as for instance the definition of the best rhythmic features and the most appropriate periodicity detection method. In order to stimulate further research, the contest results, annotations, evaluation software and part of the data are available at http://ismir2004.ismir.net/ISMIR_Contest.html