Neural entrainment is strongest to the spectral flux of slow music and depends on familiarity and beat salience

Neural activity in the auditory system synchronizes to sound rhythms, and brain environment synchronization is thought to be fundamental to successful auditory perception. Sound rhythms are often operationalized in terms of the sound's amplitude envelope. We hypothesized that, especially for music, the envelope might not best capture the complex spectrotemporal fluctuations that give rise to beat perception and synchronize neural activity. This study investigated 1) neural entrainment to different musical features, 2) tempo dependence of neural entrainment, and 3) dependence of entrainment on familiarity, enjoyment, and ease of beat perception. In this electroencephalography study, 37 human participants listened to tempo modulated music (1 to 4 Hz). Independent of whether the analysis approach was based on temporal response functions (TRFs) or reliable components analysis (RCA), the spectral flux of music, as opposed to the amplitude envelope, evoked strongest neural entrainment. Moreover, music with slower beat rates, high familiarity, and easy to perceive beats elicited the strongest neural response. Based on the TRFs, we could decode music stimulation tempo, but also perceived beat rate, even when the two differed. Our results demonstrate the importance of accurately characterizing musical acoustics in the context of studying neural entrainment, and demonstrate the sensitivity of entrainment to musical tempo, familiarity, and beat salience.

Mathematical description of the processes of synthesis of digital multispectral images

The article discusses the features of the formation of digital multispectral images corresponding to narrow registration zones. The processes of signal transformations are analyzed during the synthesis of such images from the original multispectral images with overlapping spectral flux registration zones. Questions of the mathematical description of the differential and multiplicative method for the synthesis of spectrozonal images are discussed. Analytical expressions are given that correspond to signal transformations of the original digital multispectral images in their differential and multiplicative synthesis.

LAMOST Fiber Positioning Unit Detection Based on Deep Learning

The double revolving fiber positioning unit (FPU) is one of the key technologies of The Large Sky Area Multi-Object Fiber Spectroscope Telescope (LAMOST). The positioning accuracy of the computer controlled FPU depends on robot accuracy as well as the initial parameters of FPU. These initial parameters may deteriorate with time when FPU is running in non-supervision mode, which would lead to bad fiber position accuracy and further efficiency degradation in the subsequent surveys. In this paper, we present an algorithm based on deep learning to detect the FPU’s initial angle using the front illuminated image of LAMOST focal plane. Preliminary test results show that the detection accuracy of the FPU initial angle is better than 2.°5, which is good enough to distinguish those obvious bad FPUs. Our results are further well verified by direct measurement of fiber position from the back illuminated image and the correlation analysis of the spectral flux in LAMOST survey data.

Music with Concurrent Saliences of Musical Features Elicits Stronger Brain Responses

Brain responses are often studied under strictly experimental conditions in which electroencephalograms (EEGs) are recorded to reflect reactions to short and repetitive stimuli. However, in real life, aural stimuli are continuously mixed and cannot be found isolated, such as when listening to music. In this audio context, the acoustic features in music related to brightness, loudness, noise, and spectral flux, among others, change continuously; thus, significant values of these features can occur nearly simultaneously. Such situations are expected to give rise to increased brain reaction with respect to a case in which they would appear in isolation. In order to assert this, EEG signals recorded while listening to a tango piece were considered. The focus was on the amplitude and time of the negative deflation (N100) and positive deflation (P200) after the stimuli, which was defined on the basis of the selected music feature saliences, in order to perform a statistical analysis intended to test the initial hypothesis. Differences in brain reactions can be identified depending on the concurrence (or not) of such significant values of different features, proving that coterminous increments in several qualities of music influence and modulate the strength of brain responses.

A real-time music detection method based on convolutional neural network using Mel-spectrogram and spectral flux

Audio processing, including speech enhancement system, improves speech intelligibility and quality in real-time communication (RTC) such as online meetings and online education. However, such processing, primarily noise suppression and automatic gain control, is harmful to music quality when the captured signal is music instead of speech. A music detector can solve the issue above by switching off the speech processing when the music is detected. In RTC scenarios, the music detector should be low-complexity and cover various situations, including different types of music, background noises, and other acoustical environments. In this paper, a real-time music detection method with low-computation complexity is proposed, based on a convolutional neural network (CNN) using Mel-spectrogram and spectral flux as input features. The proposed method achieves overall 90.63% accuracy under different music types (classical music, instruments solos, singing-songs, etc.), speech languages (English and Mandarin), and noise types. The proposed method is constructed on a lightweight CNN model with a small feature size, which guarantees real-time processing.

Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network

Electroencephalogram (EEG) signal is broadly utilized for monitoring epilepsy and plays a key role to revitalize close loop brain. The classical method introduced to find the seizures relies on EEG signals which is complex as well as costly, if channel count increases. This paper introduces the novel method named exponential-squirrel atom search optimization (Exp-SASO) algorithm in order to train the deep RNN for discovering epileptic seizure. Here, the input EEG signal is given to the pre-processing module for enhancing the quality of image by reducing the noise. Then, the pre-processed image is forwarded to the feature extraction module. The features, like statistical features, spectral features, logarithmic band power, wavelet coefficients, common spatial patterns, along with spectral decrease, pitch chroma, tonal power ratio, and spectral flux, are extracted. Once the features are extracted, the feature selection is carried out using fuzzy information gain model for choosing appropriate features for further processing.

Spatial-frequency features of radiation produced by a step-wise tapered undulator

A scheme to generate wide-bandwidth radiation using a step-wise tapered undulator with a segmented structure is proposed. This magnetic field configuration allows to broaden the undulator harmonic spectrum by two orders of magnitude, providing 1 keV bandwidth with spectral flux density exceeding 1016 photons s−1 mm−2 (0.1% bandwidth)−1 at 5 keV on the sample. Such a magnetic setup is applicable to superconducting devices where magnetic tapering cannot be arranged mechanically. The resulting radiation with broadband spectrum and flat-top shape may be exploited at a multipurpose beamline for scanning over the spectrum at time scales of 10–100 ms. The radiation from a segmented undulator is described analytically and derivations with numerical simulations are verified. In addition, a start-to-end simulation of an optical beamline is performed and issues related to the longitudinally distributed radiation source and its image upon focusing on the sample are addressed.

Changing Accretion Geometry of Seyfert 1 Mrk 335 with NuSTAR: A Comparative Study

We present a detailed spectral study of the narrow-line Seyfert 1 galaxy, Markarian 335, using eight epoch observations made between 2013 and 2020 with the Nuclear Spectroscopic Telescope Array. The source was variable during this period both in spectral flux and flow geometry. We estimated the height of the Compton cloud from the model fitted parameters for the whole observation period. This allowed us to investigate the underlying physical processes that drive the variability in X-rays. Our model fitted mass varies in a narrow range, between (2.44±0.45−3.04±0.56)×107M⊙, however, given the large error bars, it is consistent with being constant and is in agreement with that known from optical reverberation mapping observations. The disk mass accretion rate reached a maximum of 10% of the Eddington rate during June 2013. Our study sheds light on mass outflows from the system and also compares different aspects of accretion with X-ray binaries.

Timbre-based machine learning of clustering Chinese and Western Hip Hop music

Chinese and Western Hip Hop musical pieces are clustered using timbre-based Music Information Retrieval (MIR) and machine learning (ML) algorithms. Psychoacoustically motivated algorithms extracting timbre features such as spectral centroid, roughness, sharpness, sound pressure level (SPL), flux, etc. were extracted form 38 contemporary Chinese and 38 Western 'classical' (USA, Germany, France, Great Britain) Hip Hop pieces. All features were integrated over the pieces with respect to mean and standard deviation. A Kohonen self-organizing map, as integrated in the Computational Music and Sound Archive (COMSAR\cite{COMSAR}) and apollon\cite{apollon} framework was used to train different combinations of feature vectors in their mean and standard deviation integrations. No mean was able to cluster the corpora. Still SPL standard deviation perfectly separated Chinese and Western pieces. Spectral flux, sharpness, and spread standard deviation created two sub-cluster within the Western corpus, where only Western pieces had strong values there. Spectral centroid std did sub-cluster the Chinese Hip Hop pieces, where again only Chinese pieces had strong values. These findings point to different production, composition, or mastering strategies. E.g. the clear SPL-caused clusters point to the loudness-war of contemporary mastering, using massive compression to achieve high perceived loudness.