Spectral Flatness Research Articles

Identification, Analysis and Characterization of Base Units of Bird Vocal Communication: The White Spectacled Bulbul (Pycnonotus xanthopygos) as a Case Study.

Animal vocal communication is a broad and multi-disciplinary field of research. Studying various aspects of communication can provide key elements for understanding animal behavior, evolution, and cognition. Given the large amount of acoustic data accumulated from automated recorders, for which manual annotation and analysis is impractical, there is a growing need to develop algorithms and automatic methods for analyzing and identifying animal sounds. In this study we developed an automatic detection and analysis system based on audio signal processing algorithms and deep learning that is capable of processing and analyzing large volumes of data without human bias. We selected the White Spectacled Bulbul (Pycnonotus xanthopygos) as our bird model because it has a complex vocal communication system with a large repertoire which is used by both sexes, year-round. It is a common, widespread passerine in Israel, which is relatively easy to locate and record in a broad range of habitats. Like many passerines, the Bulbul’s vocal communication consists of two primary hierarchies of utterances, syllables and words. To extract each of these units’ characteristics, the fundamental frequency contour was modeled using a low degree Legendre polynomial, enabling it to capture the different patterns of variation from different vocalizations, so that each pattern could be effectively expressed using very few coefficients. In addition, a mel-spectrogram was computed for each unit, and several features were extracted both in the time-domain (e.g., zero-crossing rate and energy) and frequency-domain (e.g., spectral centroid and spectral flatness). We applied both linear and non-linear dimensionality reduction algorithms on feature vectors and validated the findings that were obtained manually, namely by listening and examining the spectrograms visually. Using these algorithms, we show that the Bulbul has a complex vocabulary of more than 30 words, that there are multiple syllables that are combined in different words, and that a particular syllable can appear in several words. Using our system, researchers will be able to analyze hundreds of hours of audio recordings, to obtain objective evaluation of repertoires, and to identify different vocal units and distinguish between them, thus gaining a broad perspective on bird vocal communication.

Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals.

Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively.

Flat Optical Frequency Comb Generator Based on Integrated Lithium Niobate Modulators

Chip-scale electro-optic (EO) frequency combs are expected to play an essential role in future high-capacity optical communications systems and next-generation mobile communications. The application requires integrated EO frequency comb generators featuring good spectral flatness, high modulation bandwidth, low driving voltage and low insertion loss simultaneously. Here, we demonstrate a flat-top optical flat comb (OFC) generator based on high-performance lithium niobate on insulator modulators. The OFC generator shows a low on-chip loss (2.1 dB), a low driving voltage over a broad frequency range, and a large 3-dB EO bandwidth. Moreover, with consuming power of less than 2W, the presented device produces 13 lines with a power variation of less than 1.2 dB and a line spacing of 31 GHz which can further be extended to 67 GHz.

Optimization strategy of power control for C+L+S band transmission using a simulated annealing algorithm.

To increase the transmission capacity, ultra-wideband wavelength-division multiplexing (UWB WDM) has been exploited to enlarge the spectral range. However, inter-channel stimulated Raman scattering (ISRS) results in power transition from high-frequency channels to low-frequency channels in wideband scenarios, which degrades the Q-factor of signals. Hence, we modify the optimization method of power control by applying the simulated annealing (SA) algorithm to search for the optimal power slopes and offsets of three bands to construct an optimum distribution of launch powers over channels. High transmission capacity can be reached by carrying 384 channels (96+96+192) in the C+L+S band with the consideration of dynamic Raman gain and channel-dependent parameters. We show that compared to using brute-force searching (BFS), a comparable and even higher transmission capacity can be achieved by the SA algorithm. Meanwhile, the searching speed of the SA algorithm is much faster. Also, different optimizing strategies can be selected to balance the trade-off between capacity and spectral flatness. This method can be used for designing arbitrary optical fiber UWB WDM systems before practical testing.

What a deep song: The role of music features in perceived depth.

This study examines perceptions of music depth by exploring its relationships to different music features. First, a correlation analysis shows that the perceived depth of music is negatively correlated with valence and arousal and is also related to different music features, including tempo, Mel-frequency cepstrum coefficients, chromagrams, spectral centroids, spectral bandwidth, spectral contrast, spectral flatness, spectral roll-off, and tonal centroid features. Applying machine learning methods, we find that selected music features can predict perceptions of music depth, and a random forest regression (RFR) is found to perform best in this study. Finally, a feature importance analysis shows that the principal component of spectral contrast dominates the RFR-based music depth recognition model, showing that deep music usually has clear and narrow-band audio signals.

Spectral flatness and the volume of intersections of p-ellipsoids

Motivated by classical works of Schechtman and Schmuckenschläger on intersections of ℓp-balls and recent ones in information-based complexity relating random sections of ellipsoids and the quality of random information in approximation problems, we study the threshold behavior of the asymptotic volume of intersections of generalized p-ellipsoids. The non-critical behavior is determined under a spectral flatness (Wiener entropy) condition on the semi-axes. In order to understand the critical case at the threshold, we prove a central limit theorem for q-norms of points sampled uniformly at random from a p-ellipsoid, which is obtained under Noether's condition on the semi-axes.

Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN

ABSTRACT Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more. Two basic grounds of voice or speech analysis are to detect human gender and the geographical region based on accent. This study presents a three-layer feature extraction method from the raw human voice to detect the gender as male or female, as well as the region from where that voice belongs. Fundamental frequency, spectral entropy, spectral flatness, and mode frequency have been calculated in the first layer of feature extraction. On the other hand, Mel Frequency Cepstral Coefficient has been used to extract the features in the second layer and linear predictive coding in the third layer. Regular voice contains some noises which have been removed with multiple audio data filtering processes to get noise-free smooth data. Multi-Output-based 1D Convolutional Neural Network has been used to recognize gender and region from a combined dataset which consists of TIMIT, RAVDESS, and BGC datasets. The model has successfully predicted the gender with 93.01% and region with 97.07% accuracy. This method works better than usual state-of-the-art methods in separate datasets along with the combined dataset on both gender and region classification.

Turbulent Cascade and Energy Transfer Rate in a Solar Coronal Mass Ejection

Abstract Turbulence properties are examined before, during, and after a coronal mass ejection (CME) detected by the Wind spacecraft in 2012 July. The power-law scaling of the structure functions, providing information on the power spectral density and flatness of the velocity, magnetic field, and density fluctuations, were examined. The third-order moment scaling law for incompressible, isotropic magnetohydrodynamic turbulence was observed in the preceding and trailing solar wind, as well as in the CME sheath and magnetic cloud. This suggests that the turbulence could develop sufficiently after the shock, or that turbulence in the sheath and cloud regions was robustly preserved even during the mixing with the solar wind plasma. The turbulent energy transfer rate was thus evaluated in each of the regions. The CME sheath shows an increase of energy transfer rate, as expected from the lower level of Alfvénic fluctuations and suggesting the role of the shock-wind interaction as an additional source of energy for the turbulent cascade.

Supercontinuum generated in an all-polarization-maintaining random fiber laser structure.

We demonstrated a linearly-polarized supercontinuum (SC) directly generated in an all-polarization-maintaining random fiber laser (RFL) structure. Owing to the comparatively high Raman gain of the polarization-maintaining germanium doped fiber (GDF), the spectrum of the output SC shows an enhanced bandwidth and improved spectral flatness compared to the unpolarized counterpart. The output SC has an average output power of 4.43 W with a spectrum covering from 600 nm to 1900nm. The polarization extinction ratio (PER) is measured to be greater than 18 dB from 800 nm to 1700nm at the highest output power level. To the best of our knowledge, this is the first demonstration of a linearly-polarized SC generated directly from a RFL. This work is meaningful to help further expand the bandwidth of SC generated from a RFL and provides a simple and cost-effective method of generating linearly-polarized SC for practical applications.

Ultra-flat optical frequency comb generation based on a polarization modulator and a Butterworth band-stop filter.

A new, to the best of our knowledge, scheme for the generation of ultra-flat optical frequency combs (OFCs) is proposed, which mainly uses a polarization modulator (PolM) and a first-order Butterworth band-stop filter. The PolM is driven by a digital periodic square-wave signal, and the output of the PolM is an uneven OFC. The function of the Butterworth band-stop filter is to obtain flat and stable OFCs by changing its bandwidth. The comb spacing of the OFCs can be adjusted by changing the period of the square-wave signal. The theoretical model for the scheme is also established. The simulation results show that the generator constructed according to the proposed scheme can produce 64, 128, and 256 comb lines with 200 MHz comb spacing and with spectral flatness lower than 0.3 dB. The comb spacing can be further increased by using the square-wave signal at GHz rates, and the generated OFCs exhibit good frequency tunability.

Detection and Localization of Multiple Damages through Entropy in Information Theory

According to recent works, entropy measures, and more specifically, spectral entropies, are emerging as an efficient method for the damage assessment of both mechanical systems and civil structures. Specifically, the occurrence of structural system alterations (intended in this work as stiffness reduction) can be detected as a localized change in the signal entropy. Here, the Wiener Entropy (also known as the Spectral Flatness) of strain measurements is proved as a viable tool for single and multiple damage assessment, including damage detection, localization, and severity assessment. A case study from oil & gas engineering, i.e., a finite element model of a buried steel pipeline, is utilized for this aim.

Evolution of Solar Wind Turbulence from 0.1 to 1 au during the First Parker Solar Probe–Solar Orbiter Radial Alignment

Abstract The first radial alignment between Parker Solar Probe and Solar Orbiter spacecraft is used to investigate the evolution of solar wind turbulence in the inner heliosphere. Assuming ballistic propagation, two 1.5 hr intervals are tentatively identified as providing measurements of the same plasma parcels traveling from 0.1 to 1 au. Using magnetic field measurements from both spacecraft, the properties of turbulence in the two intervals are assessed. Magnetic spectral density, flatness, and high-order moment scaling laws are calculated. The Hilbert–Huang transform is additionally used to mitigate short sample and poor stationarity effects. Results show that the plasma evolves from a highly Alfvénic, less-developed turbulence state near the Sun, to fully developed and intermittent turbulence at 1 au. These observations provide strong evidence for the radial evolution of solar wind turbulence.

High-power mid-IR supercontinuum generation in fluoroindate and arsenic sulfide fibers pumped by a broadband 1.9–2.7 μm all-fiber laser source

We report the experimental demonstration of high-power supercontinuum generation in step-index fluoroindate (InF3) and chalcogenide (As2S3) fibers. A home-built fiber-based laser system with a broad spectrum spanning from 1.9 to 2.7 μm and delivering an output average power of 6.15 W was used as the pump source. A time-averaged power of 2.95 W covering the entire 2–5 μm wavelength range is obtained from an InF3 fiber. Cascading the system by adding an As2S3 fiber, the spectrum was extended up to 5.58 μm. Moreover, we present a supercontinuum with an average power of 440 mW and a spectrum extended to 4.17 μm generated by an As2S3 fiber pumped directly by a broadband (2.4–2.7 μm), silica-based laser source centered at 2.55 µm. Combined with a high degree of spectral flatness, the high-output supercontinuum power highlights the potential of the developed sources for use in a wide variety of applications.

Optimized properties of chaos from a laser diode

We perform an experimental parametric study of the chaos generated by a laser diode subjected to phase-conjugate feedback. In addition to the typical figure of merit, i.e., chaos bandwidth, the corresponding spectral flatness and permutation entropy at delay is analyzed. Our experimental observations reveal that the chaos can be generated with a bandwidth of ≈29GHz, a spectral flatness up to 0.75, and a permutation entropy at delay of up to 0.99. These optimized performances are maintained over a large range of parameters and have not been achieved in the conventional optical feedback configuration. Interestingly, reducing the pump current reduces the chaos bandwidth while keeping the spectral flatness and the permutation entropy at delay the same as observed for increased pump current. Our experimental findings are consistent with the presented numerical simulations produced using the Lang-Kobayashi model.

Maximum Spectral Flatness Control of a Manipulandum for Human Motor System Identification

System identification of a dynamic environment using a robotic device utilizes physical perturbations in the form of displacement or force. To obtain an accurate system model, physical perturbations must be informative, which can be characterized by their spectral properties. The process of generating physical perturbations by using a robotic device often leads to spectral property degradation in the high-frequency region due to the dynamics of robot motion control and discrete-time signal processing. Spectral flatness is a metric applicable to quantifying the fidelity of the robotic system and quality of physical perturbations on an external object. This letter introduces a new metric named Band-limited Spectral Flatness Gain (BLSFG) to evaluate the physical perturbation quality relative to the input reference over a frequency band of interest. Motion control of a manipulandum that generates pseudorandom position perturbations for human sensorimotor system identification is considered as a representative example. The closed-loop system dynamics of the position control is characterized and optimized based on the BLSFG. Results suggest that a certain underdamped closed-loop property is advantageous to improve the spectral flatness of a degraded continuous-time pseudorandom reference. A high BLSFG is achieved when the resonance frequency of the closed-loop system is close to the update frequency of the pseudorandom sequence.

Supercontinuum generation of 314.7 W ranging from 390 to 2400 nm by tapered photonic crystal fiber

To consider both high-power handling and blue-extended supercontinuum (SC) generation, a long-tapered photonic crystal fiber is pumped by a high-power laser source. An SC ranging from 390 to 2400 nm with 314.7 W output power is obtained. A spectral component below 960 nm accounts for 36.1% of the total output power, exceeding 113.5 W, with a spectral flatness within 16 dB. To the best of our knowledge, this is the first time an SC coverage of all visible wavelengths with more than 300 W output power has been achieved. This result increases the output power of the SC covering the visible range by a factor of three.

Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising

With the aid of ensemble empirical mode decomposition (EEMD), de-noising of the electrocardiogram (ECG) signal based on the genetic particle filter and fuzzy thresholding is proposed in this paper, which effectively eliminates noise from the ECG signal. This paper proposes a two-phase scheme for removing noise from ECG signal. In the first phase, noisy signal is decomposed into true intrinsic mode functions (IMFs) with the help of EEMD. Adaptive EEMD (AEEMD) is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise are obtained by using spectral flatness of each IMF and fuzzy thresholding. Corrupted IMFs are filtered using genetic particle filter to remove the noise. Finally, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for different databases and it gives better signal-to-noise ratio and root mean square error than other existing techniques.

A Spectral-Based Approach for BCG Signal Content Classification.

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

Dissipative solitons in photonic molecules

Many physical systems display quantized energy states. In optics, interacting resonant cavities show a transmission spectrum with split eigenfrequencies, similar to the split energy levels that result from interacting states in bonded multi-atomic, i.e. molecular, systems. Here, we study the nonlinear dynamics of photonic diatomic molecules in linearly coupled microresonators and demonstrate that the system supports the formation of self-enforcing solitary waves when a laser is tuned across a split energy level. The output corresponds to a frequency comb (microcomb) whose characteristics in terms of power spectral distribution are unattainable in single-mode (atomic) systems. Photonic molecule microcombs are coherent, reproducible, and reach high conversion efficiency and spectral flatness whilst operated with a laser power of a few milliwatts. These properties can favor the heterogeneous integration of microcombs with semiconductor laser technology and facilitate applications in optical communications, spectroscopy and astronomy.

Longitudinally thickness-controlled nanofilms on exposed core fibres enabling spectrally flattened supercontinuum generation

Nonlinear frequency conversion is a pathway to unlock undiscovered physics and implement tailored light sources for spectroscopy or medicine. A key challenge is the establishment of spectrally flat outputs, which is particularly demanding in the context of soliton-based light conversion at low pump energy. Here, we introduce the concept of controlling nonlinear frequency conversion by longitudinally varying resonances, allowing the shaping of soliton dynamics and achieving broadband spectra with substantial spectral flatness. Longitudinally varying resonances are realised by nanofilms with gradually changing thicknesses located on the core of an advanced microstructured fibre. Nanofilms with engineered thickness profiles are fabricated by tilted deposition, representing a waveguide-compatible approach to nano-fabrication, and inducing well-controlled resonances into the system, allowing unique dispersion control along the fibre length. Key features and dependencies are examined experimentally, showing improved bandwidth and spectral flatness via multiple dispersive wave generation and dispersion-assisted soliton Raman shifts while maintaining excellent pulse-to-pulse stability and coherence in simulations, suggesting the relevance of our findings for basic science as well as tailored light sources.