Reliability-Informed Beat Tracking of Musical Signals

Abstract—A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k-nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping. Index Terms—Beat-tracking, beat quality, beat-tracking reliability, k-nearest neighbor (k-NN) regression, music signal processing. I

Apparatus for using a time interval counter to measure frequency stability

An apparatus for measuring the relative stability of two signals is disclosed comprising a means for mixing the two signals down to a beat note sine wave and for producing a beat note square wave whose upcrossings are the same as the sine wave. A source of reference frequency is supplied to a clock divider and interval counter to synchronize them and to generate a picket fence for providing a time reference grid of period shorter than the beat period. An interval counter is employed to make a preliminary measurement between successive upcrossings of the beat note square wave for providing an approximate time interval therebetween as a reference. The beat note square wave and the picket fence are then provided to the interval counter to provide an output consisting of the time difference between the upcrossing of each beat note square wave cycle and the next picket fence pulse such that the counter is ready for each upcrossing and dead time is avoided. A computer containing an algorithm for calculating the exact times of the beat note upcrossings then computes the upcrossing times

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Code choice and code-switching in Swiss-German internet relay chat rooms

In the German-speaking regions of Switzerland, dialect is spoken by all social groups in most communicative situations, Standard German being used only when prescribed. Swiss dialects rarely appeared in written form before the 1980s, apart from the genre of dialect literature. Due to the growing acceptance of informal writing styles in many European languages, dialect is increasingly employed for written personal communication, in particular in computer-mediated communication (CMC). In Swiss Internet Relay Chat (IRC) rooms, varieties of German are used side by side as all chatters have a command of both standard and dialectal varieties. Depending on the channel, the proportion of dialectal contributions can be as high as 90 percent. The choice of a particular variety depends on both individual preference and on the predominant variety used within a specific thread. In this paper I take a quantitative approach to language variation in IRC and demonstrate how such an approach can help embed qualitative research on code-switching in CMC

Comparing timing models of two Swiss German dialects

Research on dialectal varieties was for a long time concentrated on phonetic aspects of language. While there was a lot of work done on segmental aspects, suprasegmentals remained unexploited until the last few years, despite the fact that prosody was remarked as a salient aspect of dialectal variants by linguists and by naive speakers. Actual research on dialectal prosody in the German speaking area often deals with discourse analytic methods, correlating intonations curves with communicative functions (P. Auer et al. 2000, P. Gilles & R. Schrambke 2000, R. Kehrein & S. Rabanus 2001). The project I present here has another focus. It looks at general prosodic aspects, abstracted from actual situations. These global structures are modelled and integrated in a speech synthesis system. Today, mostly intonation is being investigated. However, rhythm, the temporal organisation of speech, is not a core of actual research on prosody. But there is evidence that temporal organisation is one of the main structuring elements of speech (B. Zellner 1998, B. Zellner Keller 2002). Following this approach developed for speech synthesis, I will present the modelling of the timing of two Swiss German dialects (Bernese and Zurich dialect) that are considered quite different on the prosodic level. These models are part of the project on the "development of basic knowledge for research on Swiss German prosody by means of speech synthesis modelling" founded by the Swiss National Science Foundation

Cardiotachometer with linear beat-to-beat frequency response

Cardiotachometer detects and displays the human heart rate during physiological studies. It provides linear response to the heart rate, records heart rate during rest and under heavy stress, provides a beat-to-beat indication of changes in heart rate, and is relatively free of interfering signals from activities other than the heart rate