arXiv subjects

Dipak Ghosh

Publications and source records attributed to Dipak Ghosh.

At least 19 recordsLinked to original sources

arXiv2023

Net-charge particle ratio fluctuations in $pp$ collisions at several LHC energies

Event-by-event particle ratio fluctuations for simulated data sets of three different models named UrQMD, AMPT, and Pythia are studied using the fluctuation variable $ν_{dyn}$. The simulated data sets produced in $pp$ collisions at four different LHC energies $\sqrt{s} = 2.76, 5.02, 7$ and $13$ TeV are generated and considered for this analysis. The variation of fluctuation parameter $ν_{dyn}$ for accepted pair of meson and baryon combination $i.e.$ $[π, K]$, $[π, p]$ and $[p, K]$ with the increasing value of the mean multiplicity of charged particles ($\langle N_{ch} \rangle$) are investigated. It has been observed that the correlation between the particle pair $[π, K]$ is more than that of the two other particle pairs $[π, p]$ and $[p, K]$. However, the energy-wise inspection of the fluctuation variable $ν_{dyn}$ for $0-10\%$ centrality data shows the increase in the correlation between the particles in each pair for all three models considered.

hep-ph↗

arXiv2022

Complexity Analysis of Wind Energy, Wind Speed and Wind Direction in the light of nonlinear technique

Wind energy has an inherent intermittent character due to certain inevitable factors of nature, such as availability of wind at different weather conditions, wind direction etc. To study the intermittent character of wind energy, its daily data along with the two other important quantities, wind speed and wind direction measured in a "showcase" wind farm for a span of ten years are analyzed applying a nonlinear robust tool Multifractal Detrended Cross-correlation Analysis (MFDXA). MFDXA is a meticulous application for computation of cross-correlation between simultaneously measured nonstationary time series. Significant difference in degree of multifractality is observed for wind energy, wind speed and wind direction. Wind direction is found to possess the highest degree of multifractality implying that the degree of complexity of wind direction is higher than wind speed or energy. Further strong cross-correlation between wind energy and wind direction is an indication that the direction of wind is one of the crucial factors in generation of wind energy. Thus, the cross-correlation analysis between wind energy - wind speed, and between wind energy - wind direction gives significant information about the scaling behavior, which may have necessary inputs towards optimization of wind power generation.

physics.ao-ph↗

arXiv2021

Ragas in Bollywood music A microscopic view through multrifractal cross-correlation method

Since the start of Indian cinema, a number of films have been made where a particular song is based on a certain raga. These songs have been taking a major role in spreading the essence of classical music to the common people, who have no formal exposure to classical music. In this paper, we look to explore what are the particular features of a certain raga which make it understandable to common people and enrich the song to a great extent. For this, we chose two common ragas of Hindustani classical music, namely "Bhairav" and "Mian ki Malhar" which are known to have widespread application in popular film music. We have taken 3 minute clips of these two ragas from the renderings of two eminent maestros of Hindustani classical music. 3 min clips of ten (10) widely popular songs of Bollywood films were selected for analysis. These were analyzed with the help of a latest non linear analysis technique called Multifractal Detrended Cross correlation Analysis (MFDXA). With this technique, all parts of the Film music and the renderings from the eminent maestros are analyzed to find out a cross correlation coefficient (γx) which gives the degree of correlation between these two signals. We hypothesize that the parts which have the highest degree of cross correlation are the parts in which that particular raga is established in the song. Also the variation of cross correlation coefficient in the different parts of the two samples gives a measure of the modulation that is executed by the singer. Thus, in nutshell we try to study scientifically the amount of correlation that exists between the raga and the same raga being utilized in Film music. This will help in generating an automated algorithm through which a naïve listener will relish the flavor of a particular raga in a popular film song. The results are discussed in detail.

cs.SD↗

arXiv2021

Variation of singing styles within a particular Gharana of Hindustani classical music A nonlinear multifractal study

Hindustani classical music is entirely based on the "Raga" structures. In Hindustani music, a "Gharana" or school refers to the adherence of a group of musicians to a particular musical style. Gharanas have their basis in the traditional mode of musical training and education. Every Gharana has its own distinct features; though within a particular Gharana, significant differences in singing styles are observed between generations of performers, which can be ascribed to the individual creativity of that singer. This work aims to study the evolution of singing style among four artists of four consecutive generations from Patiala Gharana. For this, alap and bandish parts of two different Ragas sung by the four artists were analyzed with the help of non linear multifractal analysis (MFDFA) technique. The multifractal spectral width obtained from the MFDFA method gives an estimate of the complexity of the signal. The observations from the variation of spectral width give a cue towards the scientific recognition of Guru-Shisya Parampara (teacher-student tradition) - a hitherto much-heard philosophical term. From a quantitative approach this study succeeds in analyzing the evolution of singing styles within a particular Gharana over generations of artists as well as the effect of globalization in the field of classical music.

cs.SD↗

arXiv2021

Language Independent Emotion Quantification using Non linear Modelling of Speech

At present emotion extraction from speech is a very important issue due to its diverse applications. Hence, it becomes absolutely necessary to obtain models that take into consideration the speaking styles of a person, vocal tract information, timbral qualities and other congenital information regarding his voice. Our speech production system is a nonlinear system like most other real world systems. Hence the need arises for modelling our speech information using nonlinear techniques. In this work we have modelled our articulation system using nonlinear multifractal analysis. The multifractal spectral width and scaling exponents reveals essentially the complexity associated with the speech signals taken. The multifractal spectrums are well distinguishable the in low fluctuation region in case of different emotions. The source characteristics have been quantified with the help of different non-linear models like Multi-Fractal Detrended Fluctuation Analysis, Wavelet Transform Modulus Maxima. The Results obtained from this study gives a very good result in emotion clustering.

cs.SD↗

arXiv2021

A Fractal Approach to Characterize Emotions in Audio and Visual Domain: A Study on Cross-Modal Interaction

It is already known that both auditory and visual stimulus is able to convey emotions in human mind to different extent. The strength or intensity of the emotional arousal vary depending on the type of stimulus chosen. In this study, we try to investigate the emotional arousal in a cross-modal scenario involving both auditory and visual stimulus while studying their source characteristics. A robust fractal analytic technique called Detrended Fluctuation Analysis (DFA) and its 2D analogue has been used to characterize three (3) standardized audio and video signals quantifying their scaling exponent corresponding to positive and negative valence. It was found that there is significant difference in scaling exponents corresponding to the two different modalities. Detrended Cross Correlation Analysis (DCCA) has also been applied to decipher degree of cross-correlation among the individual audio and visual stimulus. This is the first of its kind study which proposes a novel algorithm with which emotional arousal can be classified in cross-modal scenario using only the source audio and visual signals while also attempting a correlation between them.

cs.SD↗

arXiv2021

Neural Network architectures to classify emotions in Indian Classical Music

Music is often considered as the language of emotions. It has long been known to elicit emotions in human being and thus categorizing music based on the type of emotions they induce in human being is a very intriguing topic of research. When the task comes to classify emotions elicited by Indian Classical Music (ICM), it becomes much more challenging because of the inherent ambiguity associated with ICM. The fact that a single musical performance can evoke a variety of emotional response in the audience is implicit to the nature of ICM renditions. With the rapid advancements in the field of Deep Learning, this Music Emotion Recognition (MER) task is becoming more and more relevant and robust, hence can be applied to one of the most challenging test case i.e. classifying emotions elicited from ICM. In this paper we present a new dataset called JUMusEmoDB which presently has 400 audio clips (30 seconds each) where 200 clips correspond to happy emotions and the remaining 200 clips correspond to sad emotion. For supervised classification purposes, we have used 4 existing deep Convolutional Neural Network (CNN) based architectures (resnet18, mobilenet v2.0, squeezenet v1.0 and vgg16) on corresponding music spectrograms of the 2000 sub-clips (where every clip was segmented into 5 sub-clips of about 5 seconds each) which contain both time as well as frequency domain information. The initial results are quite inspiring, and we look forward to setting the baseline values for the dataset using this architecture. This type of CNN based classification algorithm using a rich corpus of Indian Classical Music is unique even in the global perspective and can be replicated in other modalities of music also. This dataset is still under development and we plan to include more data containing other emotional features as well. We plan to make the dataset publicly available soon.

cs.SD↗

arXiv2020

Acoustical classification of different speech acts using nonlinear methods

A recitation is a way of combining the words together so that they have a sense of rhythm and thus an emotional content is imbibed within. In this study we envisaged to answer these questions in a scientific manner taking into consideration 5 (five) well known Bengali recitations of different poets conveying a variety of moods ranging from joy to sorrow. The clips were recited as well as read (in the form of flat speech without any rhythm) by the same person to avoid any perceptual difference arising out of timbre variation. Next, the emotional content from the 5 recitations were standardized with the help of listening test conducted on a pool of 50 participants. The recitations as well as the speech were analyzed with the help of a latest non linear technique called Detrended Fluctuation Analysis (DFA) that gives a scaling exponent α, which is essentially the measure of long range correlations present in the signal. Similar pieces (the parts which have the exact lyrical content in speech as well as in the recital) were extracted from the complete signal and analyzed with the help of DFA technique. Our analysis shows that the scaling exponent for all parts of recitation were much higher in general as compared to their counterparts in speech. We have also established a critical value from our analysis, above which a mere speech may become a recitation. The case may be similar to the conventional phase transition, wherein the measurement of external condition at which the transformation occurs (generally temperature) is called phase transition. Further, we have also categorized the 5 recitations on the basis of their emotional content with the help of the same DFA technique. Analysis with a greater variety of recitations is being carried out to yield more interesting results.

eess.AS↗

arXiv2020

Speaker Recognition in Bengali Language from Nonlinear Features

At present Automatic Speaker Recognition system is a very important issue due to its diverse applications. Hence, it becomes absolutely necessary to obtain models that take into consideration the speaking style of a person, vocal tract information, timbral qualities of his voice and other congenital information regarding his voice. The study of Bengali speech recognition and speaker identification is scarce in the literature. Hence the need arises for involving Bengali subjects in modelling our speaker identification engine. In this work, we have extracted some acoustic features of speech using non linear multifractal analysis. The Multifractal Detrended Fluctuation Analysis reveals essentially the complexity associated with the speech signals taken. The source characteristics have been quantified with the help of different techniques like Correlation Matrix, skewness of MFDFA spectrum etc. The Results obtained from this study gives a good recognition rate for Bengali Speakers.

cs.SD↗

arXiv2019

Study of Di-muon Production Process in $pp$ Collision in CMS Data from Symmetry Scaling Perspective

A deailed knowledge of pp collision is required both as input to comprehensive theoretical models of strong interactions and as baseline to decipher the AA collisions at relativistic and ultrarelativistic energies, which has been of great interest in the area of theoretical and experimental physics. The multiplicity distribution of particles produced in pp collisions and the multiplicity dependence of various global event features serve as rudimentary observables which reflect the features of the inherent dynamics of the process of particle production. Recent availability of dimuon data has triggered spur of interests in revisiting strong interaction process, the study of which in detail is extremely important for enhancement of our understanding on not only the theory of strong interaction but also possible physics scenarios beyond the standard model. Numerous papers have come up where background of production process of dimuon in pp collision has been discussed and analyzed particularly for production of dimuon from γγ interaction. Apart from conventional approaches the present authors proposed a new approach with successful application in context of symmetry scaling in AA collision data from ALICE, pp collisions at 8TeV from CMS and so on. The different approach essentially analyses fluctuation pattern from the perspective of symmetry scaling or degree of self-similarity involved in the process. The proposed methods of analysis using pseudorapidity values of di-muon data taken from the primary dataset of RunA(2011)-7TeV and RunB(2012)-8TeV of the pp collision from CMS, reveal that pseudorapidity spaces corresponding to different ranges of rapidity are highly scale-free and the scaling pattern changes from one rapidity range to another at both energy. Also, the degree of cross-correlation between rapidity and azimuthal space has been found to follow the similar behavior.

hep-ex↗

arXiv2019

Symmetry-Scaling Based Complex Network Approach to Explore Exotic Hadronic States in High-Energy Collision

Conventionally invariant mass or transverse momentum techniques have been used to probe for any formation of some exotic or unusual resonance states in high energy collision. In this work, we have applied symmetry scaling based complex network approach to study exotic resonance/hadronic states utilizing the clustering coefficients and associated scaling parameter extracted with the complex network based technique of Visibility Graph. We have analyzed the data of Pb-Pb collision data sample at 2.76 TeV from ALICE Collaboration and analyzed different patterns of symmetry scaling, scale-freeness, correlation and clustering among the produced particles. This is a chaos-based complex network technique where simple parameters like Average Clustering Coefficient and Power of Scale-freeness of Visibility Graph(PSVG) may hint at formation of some exotic or unusual resonance states without using conventional methods. From this experiment we may infer that highest range of Average clustering coefficient, might be the resonance states/clusters from where the hadronic decay might have occurred and few clusters with highest value of this parameter may indicate that those clusters may be the ancestors of some strange particles. There have also been extensive study of dilepton production since the study of lepton pair generation in Drell-Yan processes is immensely important as these processes enable us to validate the Standard Model-SM predictions for the fundamental particles interaction at the new energy region and also to probe for new physics beyond SM. Hence we have applied the same methodology and extracted the same parameters for p-p collision data at 8TeV from CMS, to detect possible resonance states eventually generating lepton pairs.

hep-ph↗

arXiv2018

Pion Fluctuation Study in Pb-Pb Collision at 2.76 TeV per nucleon pair from ALICE Experiment with Chaos and Complex Network-based Methods

Chaos and complex-network based study is performed to look for signature of phase transition in Pb-Pb collision data sample at 2.76TeV per nucleon pair from ALICE Collaboration. The analysis is done on the pseudorapidity values extracted from the data of ALICE experiment and the methods used are Multifractal-Detrended-Fluctuation-Analysis(MF-DFA), and a rigorous chaos-based, complex-network based method - Visibility-Graph(VG) analysis. The fractal behavior of pionisation process is studied by utilizing MF-DFA method for extracting the Hurst exponent and Multifractal-spectrum-width to analyze the scale-freeness and fractality inherent in the fluctuation pattern of eta. Then VG method is used to analyze the fluctuation from a completely different perspective of complex network. This algorithm's scale-freeness detection mechanism to extract the Power-of-Scale-freeness-of-Visibility-Graph(PSVG), re-establishes the scale-freeness and fractality. Earlier, it has been shown that the scaling behavior is different from one hadron-nucleus($π^{-}$-AgBr(350 GeV)) to one nucleus-nucleus($^{32}$S-AgBr(200 A GeV)) interaction which is of comparatively higher total energy. In this work, we have compared the fluctuation pattern in terms of 3 rigorous parameters - Multifractal-spectrum-width, Hurst exponent and PSVG, between Pb-Pb(2.76TeV per nucleon pair) data and either of $π^{-}$-AgBr(350 GeV) or $^{32}$S-AgBr(200 A GeV) data, where both the interaction data are of significantly less energy than the ALICE data. We found that the values of the 3 parameters are substantially different for ALICE data compared to the other two interaction data. As remarkably different value of long-range-correlation indicates phase-transition, similar change in the fluctuation pattern in terms of these parameters can be attributed to a phase-transition and also the onset of QGP.

hep-ex↗

arXiv2018

Detecting tala Computationally in Polyphonic Context - A Novel Approach

In North-Indian-Music-System(NIMS),tabla is mostly used as percussive accompaniment for vocal-music in polyphonic-compositions. The human auditory system uses perceptual grouping of musical-elements and easily filters the tabla component, thereby decoding prominent rhythmic features like tala, tempo from a polyphonic composition. For Western music, lots of work have been reported for automated drum analysis of polyphonic composition. However, attempts at computational analysis of tala by separating the tabla-signal from mixed signal in NIMS have not been successful. Tabla is played with two components - right and left. The right-hand component has frequency overlap with voice and other instruments. So, tala analysis of polyphonic-composition, by accurately separating the tabla-signal from the mixture is a baffling task, therefore an area of challenge. In this work we propose a novel technique for successfully detecting tala using left-tabla signal, producing meaningful results because the left-tabla normally doesn't have frequency overlap with voice and other instruments. North-Indian-rhythm follows complex cyclic pattern, against linear approach of Western-rhythm. We have exploited this cyclic property along with stressed and non-stressed methods of playing tabla-strokes to extract a characteristic pattern from the left-tabla strokes, which, after matching with the grammar of tala-system, determines the tala and tempo of the composition. A large number of polyphonic(vocal+tabla+other-instruments) compositions has been analyzed with the methodology and the result clearly reveals the effectiveness of proposed techniques.

cs.SD↗

arXiv2017

Music of Brain and Music on Brain: A Novel EEG Sonification approach

Can we hear the sound of our brain? Is there any technique which can enable us to hear the neuro-electrical impulses originating from the different lobes of brain? The answer to all these questions is YES. In this paper we present a novel method with which we can sonify the Electroencephalogram (EEG) data recorded in rest state as well as under the influence of a simplest acoustical stimuli - a tanpura drone. The tanpura drone has a very simple yet very complex acoustic features, which is generally used for creation of an ambiance during a musical performance. Hence, for this pilot project we chose to study the correlation between a simple acoustic stimuli (tanpura drone) and sonified EEG data. Till date, there have been no study which deals with the direct correlation between a bio-signal and its acoustic counterpart and how that correlation varies under the influence of different types of stimuli. This is the first of its kind study which bridges this gap and looks for a direct correlation between music signal and EEG data using a robust mathematical microscope called Multifractal Detrended Cross Correlation Analysis (MFDXA). For this, we took EEG data of 10 participants in 2 min 'rest state' (i.e. with white noise) and in 2 min 'tanpura drone' (musical stimulus) listening condition. Next, the EEG signals from different electrodes were sonified and MFDXA technique was used to assess the degree of correlation (or the cross correlation coefficient) between tanpura signal and EEG signals. The variation of γx for different lobes during the course of the experiment also provides major interesting new information. Only music stimuli has the ability to engage several areas of the brain significantly unlike other stimuli (which engages specific domains only).

q-bio.NC↗

arXiv2017

Ring like correlation in relativistic heavy ion collision: an experimental probe using continuous wavelet transform approach

Continuous wavelet transform approach has been applied to the pseudo-rapidity distribution of shower particles produced in 16O-AgBr interactions at 60 AGeV and 32S-AgBr interactions at 200 AgeV. Multiscale analysis of wavelet pseudo-rapidity spectra has been performed in order to find out the presence of ring-like correlation, which could be either due to production of Cherenkov gluons or due to propagation of Mach Shock wave through excited nuclear matter. This approach fulfils the basic requirement of both effects that they lead to an overabundance of considered particles at some typical pseudo-rapidities. Comparison of experimental results with that obtained from analyzing events generated by FRITIOF code are not reproduced.

hep-ex↗

arXiv2017

Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA

The present study reports interesting findings in regard to emotional arousal based activities while listening to two Hindustani classical ragas of contrast emotion. EEG data was taken on 5 naive listeners while they listened to two ragas Bahar and Mia ki Malhar which are conventionally known to portray contrast emotions. The EEG data were analyzed with the help of two robust non linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation Analysis (DFA). A comparative study of the Hurst Exponents obtained from the two methods have been shown which shows that DFA provides more rigorous results compared to AFA when it comes to the scaling analysis of biosignal data. The results and implications have been discussed in detail.

q-bio.NC↗

arXiv2017

Can Musical Emotion Be Quantified With Neural Jitter Or Shimmer? A Novel EEG Based Study With Hindustani Classical Music

The term jitter and shimmer has long been used in the domain of speech and acoustic signal analysis as a parameter for speaker identification and other prosodic features. In this study, we look forward to use the same parameters in neural domain to identify and categorize emotional cues in different musical clips. For this, we chose two ragas of Hindustani music which are conventionally known to portray contrast emotions and EEG study was conducted on 5 participants who were made to listen to 3 min clip of these two ragas with sufficient resting period in between. The neural jitter and shimmer components were evaluated for each experimental condition. The results reveal interesting information regarding domain specific arousal of human brain in response to musical stimuli and also regarding trait characteristics of an individual. This novel study can have far reaching conclusions when it comes to modeling of emotional appraisal. The results and implications are discussed in detail.

q-bio.NC↗

arXiv2017

Neural (EEG) Response during Creation and Appreciation: A Novel Study with Hindustani Raga Music

What happens inside the performers brain when he is performing and composing a particular raga. Are there some specific regions in brain which are activated when an artist is creating or imaging a raga in his brain. Do the regions remain the same when the artist is listening to the same raga sung by him. These are the questions that perplexed neuroscientists for a long time. In this study we strive to answer these questions by using latest state of the art techniques to assess brain response. An EEG experiment was conducted for two eminent performers of Indian classical music, when they mentally created the imagery of a raga Jay Jayanti in their mind, as well as when they listened to the same raga. The beauty of Hindustani music lies in the fact that the musician is himself the composer and recreates the imagery of the raga in his mind while performing, hence the scope of creative improvisations are immense. The alpha and theta frequency rhythms were segregated from each of the time series data and analyzed using robust non MFDXA technique to quantitatively assess the degree of cross-correlation of each EEG frequency rhythm in different combination of electrodes from frontal, occipital and temporal lobes. A strong response was found in the occipital and fronto occipital region during mental improvisation of the raga, which is an interesting revelation of this study. Strong retentive features were obtained in regard to both alpha and theta rhythms in musical listening in the fronto temporal and occipital temporal region while the features were almost absent in the thinking part. Further, other specific regions have been identified separately for the two separate conditions in which the correlations among the different lobes were the strongest.

q-bio.NC↗