Distinct Noise Sources Research Articles

Physics-informed tracking of qubit fluctuations

Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources. Published by the American Physical Society 2024

New Approaches for Analysis of Jet Noise Data

Despite the significant advances in Computational Aeroacoustics for predicting jet noise, there is still a considerable need for experimental data. However, traditional measures of the directivity of Overall Sound Pressure Levels or the spectral distribution of the acoustic energy yield limited understanding of the nature of the sources. There is hence a need to use different approaches to the analysis of jet acoustics, among them applications of statistical methods such as auto correlation, self-correlation etc to understand the noise sources. In this paper, the acoustic characteristics of supersonic and high subsonic jets from nozzles of different exit geometries are measured and analyzed using these methods. The results show that these statistical methods of analysis can reveal the presence of two distinct noise sources in the flow and are applicable to both subsonic and supersonic flows. The information can be used to determine the appropriate model for the data and is useful for forming new theories about the noise generation mechanism in turbulent jets.

Reliability of resting-state functional connectivity in the human spinal cord: Assessing the impact of distinct noise sources

The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability – due to the removal of stable and participant-specific noise patterns – whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.

Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions

Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data.

Efficient population coding depends on stimulus convergence and source of noise.

Sensory organs transmit information to downstream brain circuits using a neural code comprised of spikes from multiple neurons. According to the prominent efficient coding framework, the properties of sensory populations have evolved to encode maximum information about stimuli given biophysical constraints. How information coding depends on the way sensory signals from multiple channels converge downstream is still unknown, especially in the presence of noise which corrupts the signal at different points along the pathway. Here, we calculated the optimal information transfer of a population of nonlinear neurons under two scenarios. First, a lumped-coding channel where the information from different inputs converges to a single channel, thus reducing the number of neurons. Second, an independent-coding channel when different inputs contribute independent information without convergence. In each case, we investigated information loss when the sensory signal was corrupted by two sources of noise. We determined critical noise levels at which the optimal number of distinct thresholds of individual neurons in the population changes. Comparing our system to classical physical systems, these changes correspond to first- or second-order phase transitions for the lumped- or the independent-coding channel, respectively. We relate our theoretical predictions to coding in a population of auditory nerve fibers recorded experimentally, and find signatures of efficient coding. Our results yield important insights into the diverse coding strategies used by neural populations to optimally integrate sensory stimuli in the presence of distinct sources of noise.

The circadian oscillator analysed at the single‐transcript level

The circadian clock is an endogenous and self‐sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well‐described in populations of cells, the single‐cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single‐molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev‐erbα (Nr1d1), Cry1 and Bmal1, in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell‐to‐cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single‐cell mRNA counts and suggest that circadian phase in single cells is encoded post‐transcriptionally.

Roles of cellular heterogeneity, intrinsic and extrinsic noise in variability of p53 oscillation

The p53 protein is a key mediator of the cellular response to various stress signals. In response to DNA damage, the concentration of p53 can temporally oscillate with fluctuations in both the amplitude and period. The underlying mechanism for p53 variability is not fully understood. Here, we construct a core regulatory network of p53 dynamics comprising the ATM-p53-Wip1 and p53-Mdm2 negative feedback loops. We dissect the contributions of cellular heterogeneity, intrinsic noise, and multiple forms of extrinsic noise to p53 variability in terms of the coefficients of variation of four quantities. Cellular heterogeneity greatly determines the fraction of oscillating cells among a population of isogenic cells. Intrinsic noise—fluctuation in biochemical reactions–has little impact on p53 variability given large amounts of molecules, whereas extrinsic colored noise with proper strength and correlation time contributes much to oscillatory variability in individual cells. With the three sources of noise combined, our results reproduce the experimental observations, suggesting that the long correlation time of colored noise is essential to p53 variability. Compared with previous studies, the current work reveals both the individual and integrated effects of distinct noise sources on p53 variability. This study provides a framework for exploring the variability in oscillations in cellular signaling pathways.

Interplay of sources of stochastic noise in a resource-based model

We introduce a stochastic resource-based model in which the agents acquire resources from the environment. The model dynamics is described by two coupled stochastic differential equations with distinct sources of noise, one acting on the population size and the other affecting the availability of resources. Random fluctuations from the environment and the population dynamics itself are simulated as stochastic effects causing variations in the resource influx and death rates. We derive analytical results for the probability density of the population size and amount of available resource when a single source exists. The analytical expressions and numerical results, that uses the Milstein approach, show a remarkable agreement. In particular, the presence of stochasticity in the death rate leads to an effectively higher death rate in comparison to the deterministic counterpart of the model. On the other hand, when the two sources of stochastic fluctuations are considered, interesting antagonistic effects are revealed by the Pearson coefficient of the correlation between the population size and resource availability. Indeed, small fluctuations in resource availability reveal strong negative correlations. In contrast, when the noise in the death rate is too low the equilibrium population size and available resource become nearly uncorrelated. We also generalize the model to handle an arbitrary number of species and resources. This allows us to probe the underlying conditions for coexistence among species. We find a stable coexistence of many species over a wide range of parameter values. In the most general conditions, the system does not violate the competitive exclusion principle.

CRSS-LDNN: Long-duration naturalistic noise corpus containing multi-layer noise recordings for robust speech processing

Multi-layer noise refers to scenarios where multiple distinct noise sources are simultaneously active in an audio stream. We collected a corpus named the CRSS long-duration naturalistic noise (CRSS-LDNN) corpus. It contains noise captured from complex daily-life activities using wearable LENA units. The diversity in noise-sources include construction noise, noise like multi-speaker babble, large-crowd noise, vehicle/bus noise on the road, home environment noise, etc. Corpus contains recordings of complex mixtures of these noise types. The babble noise and bus-engine noise present along with occasional impulsive-noise over long-duration is an example of such scenario. The data were collected at 16 kHz sampling rate with 16-bit precision in .wav format. This data would be released to speech community (http://crss.utdallas.edu). During the summer semester, a CRSS student wore a LENA device that was switched ON when multiple noise-sources were present. This corpus was recorded in naturalistic scenarios with uncontrolled mixing of various noise-sources. It provides naturalistic multi-layer noise recordings for evaluation of robust speech algorithms such as speech recognition, speaker diarization and verification, sentiment analysis. It consists of approximately 19 hours noise recordings. The CRSS-LDNN noise is more challenging as compared to existing noise corpora such as NOISEX that contains only single noise.

Gravitational wave detection using laser interferometry beyond the standard quantum limit.

Interferometric gravitational wave detectors (such as advanced LIGO) employ high-power solid-state lasers to maximize their detection sensitivity and hence their reach into the universe. These sophisticated light sources are ultra-stabilized with regard to output power, emission frequency and beam geometry; this is crucial to obtain low detector noise. However, even when all laser noise is reduced as far as technically possible, unavoidable quantum noise of the laser still remains. This is a consequence of the Heisenberg Uncertainty Principle, the basis of quantum mechanics: in this case, it is fundamentally impossible to simultaneously reduce both the phase noise and the amplitude noise of a laser to arbitrarily low levels. This fact manifests in the detector noise budget as two distinct noise sources-photon shot noise and quantum radiation pressure noise-which together form a lower boundary for current-day gravitational wave detector sensitivities, the standard quantum limit of interferometry. To overcome this limit, various techniques are being proposed, among them different uses of non-classical light and alternative interferometer topologies. This article explains how quantum noise enters and manifests in an interferometric gravitational wave detector, and gives an overview of some of the schemes proposed to overcome this seemingly fundamental limitation, all aimed at the goal of higher gravitational wave event detection rates.This article is part of a discussion meeting issue 'The promises of gravitational-wave astronomy'.

Wavepacket modeling of the jet noise source

This study is motivated by the need for physical models for the jet noise source to be used in practical noise prediction schemes for propulsion–airframe integration concepts. The basis for the source model is an amplitude-modulated traveling wave—the wavepacket. The source is parameterized and the parameters are determined by minimizing the difference between the modeled and experimental sound intensity distributions in the far field. Even though the pressure signal that reaches the far field is highly filtered, sufficient information is available to construct a wavepacket with reasonable physical characteristics. A simple stochastic extension of this concept shows a connection between the shape of the far-field sound pressure level spectrum and the emission polar angle. It suggests that the broadening of the spectrum with increasing polar angle from the downstream axis can be explained on the basis of a single noise source (the wavepacket), rather than the prevailing model of two distinct noise sources, one coherent and the other incoherent.

Stochastic analysis of a full system of two competing populations in a chemostat

This paper formulates two 3D models using stochastic differential equations (SDEs) of two microbial populations in a chemostat competing over a single substrate. These models have two distinct noise sources. One is general noise whereas the other is dilution-rate-induced noise. Nonlinear Monod growth rates are assumed and the paper is mainly focused on the parameter values where coexistence is found in the deterministic model. Nondimensionalising the equations around the point of intersection of the two growth rates identifies the dimensionless substrate feed as a large parameter. This in turn is used to perform an asymptotic analysis leading to a reduced 2D system of equations describing the dynamics of the populations on and close to a line of steady states obtained previously from the deterministic stability analysis. That reduced system allows the formulation of a spatially 2D Fokker-Planck equation which, when solved numerically, admits results similar to those from the SDEs. Contrary to previous suggestions, one particular population becomes dominant at large times. Finally, we briefly explore the case where death rates are included.

Automatic detection of noisy channels in fNIRS signal based on correlation analysis

BackgroundfNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels. MethodsIn the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers. ResultsThe validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm. Comparison with existing method(s)Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations). ConclusionsOur results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost.

Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique used to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain. In this study, we present a decomposition approach based on single-channel independent component analysis (scICA) to investigate the contribution of physiological noise to fNIRS signals during rest. Single-channel ICA is an underdetermined decomposition method, which separates a single time series into components containing nonredundant spectral information. Using scICA, fNIRS signals from a total of 17 subjects were decomposed into the constituent physiological components. The percentage contribution of the classes of physiology to the fNIRS signals including low-frequency (LF) fluctuations, respiration, and cardiac oscillations was estimated using spectral domain classification methods. Our results show that LF oscillations accounted for 40% to 55% of total power of both the oxy-Hb and deoxy-Hb signals. Respiration and its harmonics accounted for 10% to 30% of the power, and cardiac pulsations and cardio-respiratory components accounted for 10% to 30%. We describe this scICA method for decomposing fNIRS signals, which unlike other approaches to spatial covariance reduction is applicable to both single- or multiple-channel fNIRS signals and discuss how this approach allows functionally distinct sources of noise with disjoint spectral support to be separated from obscuring systemic physiology.

Fourier Analysis of Beamforming Data at the Tip of an Axial Fan Rotor

Axial fans operate in large numbers, often in the close vicinity of humans, therefore reducing their noise is a primary concern. In this paper, beamforming is applied to get information about the location and strength of noise sources on the rotor. This information is invaluable, however, difficult to interpret because of some limitations of the beamforming method. The broadband noise radiated in the upstream direction by an axial fan rotor has been examined by means of a phased array microphone system, and the recorded data have been processed with the use of the Rotating Source Identifier beamforming algorithm. In order to reduce the complexity of the source maps, spatial Fourier analysis has been applied to the beamforming-based circumferential source strength distribution taken at the tip radius of the rotor, where the complexity of the local aerodynamic phenomena makes the evaluation of the aeroacoustic noise source data especially challenging. As part of the processing method, a criterion has been introduced for identifying the significant components of the noise sources. The Fourier analysis enabled the effective enhancement of distinct noise sources out of the beamforming database, despite the presence of signal perturbations and limitations. The noise sources have been located and quantified with use of the Fourier transformation and with help of a criterion based on the physical properties of the noise generation mechanisms.

Activation process in excitable systems with multiple noise sources: One and two interacting units.

We consider the coaction of two distinct noise sources on the activation process of a single excitable unit and two interacting excitable units, which are mathematically described by the Fitzhugh-Nagumo equations. We determine the most probable activation paths around which the corresponding stochastic trajectories are clustered. The key point lies in introducing appropriate boundary conditions that are relevant for a class II excitable unit, which can be immediately generalized also to scenarios involving two coupled units. We analyze the effects of the two noise sources on the statistical features of the activation process, in particular demonstrating how these are modified due to the linear or nonlinear form of interactions. Universal properties of the activation process are qualitatively discussed in the light of a stochastic bifurcation that underlies the transition from a stochastically stable fixed point to continuous oscillations.

Abstract 3763: How stochastic single-cell fate decisions drive population dynamics in oncogene-addicted cancer

Abstract Cancer cells within a tumor environment exhibit a complex and adaptive nature whereby genetically and epigenetically distinct subpopulations compete for resources. The probabilistic nature of gene expression and intracellular molecular interactions confer a significant amount of stochasticity in cell fate decisions. This cellular heterogeneity is believed to underlie cases of cancer recurrence, acquired drug resistance, and so-called exceptional responders. From a population dynamics perspective, clonal heterogeneity and cell-fate stochasticity are distinct sources of noise, the former arising from genetic mutations and/or epigenetic transitions, extrinsic to the fate decision signaling pathways and the latter being intrinsic to biochemical reaction networks. Here, we present our results and ongoing work of a kinetic modeling study based on experimental time course data for EGFR-addicted non-small cell lung cancer (PC9) cells in both parental and isolated sublines. When PC9 cells are treated with erlotinib, an EGFR inhibitor, a complex array of division and death cell decisions arise within a given population in response to treatment. Although deterministic (ODE) simulations capture the ef-fects of clonal heterogeneity and describe the overall trends of experimentally treated tumor cell popu-lations, these are not capable of explaining the observed variability of drug response trajectories, in-cluding response magnitude and time to rebound. Our stochastic simulations, instead, capture the ef-fects of intrinsically noisy cell fate decisions that cause significant variability in cell population trajecto-ries. These findings indicate that stochastic simulations are necessary to distinguish the contribution of extrinsic (clonal heterogeneity) and intrinsic (cell fate decisions) noise to understand the variability of cancer-cell response treatment. Furthermore, they suggest that, whereas tumors with distinct clon-al structures are expected to behave differently in response to drug, two clonally identical tumors may also experience vastly different outcomes, such as exceptional response vs. rapid relapse, due to the intrinsic noise of cell fate decisions. The results of this study underscore the need to improve our un-derstanding of intracellular signaling networks that govern division vs. death decisions in cancer cells at the single-cell level. We will present our ongoing work to develop mechanistic models that link erlo-tinib-driven EGFR inhibition and intrinsic apoptotic execution. Our preliminary results give insight into what signaling interactions are required to generate the intrinsic apoptotic response to EGFR inhibi-tion with erlotinib. Citation Format: Erin M. Shockley, Leonard A. Harris, Carlos F. Lopez. How stochastic single-cell fate decisions drive population dynamics in oncogene-addicted cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3763. doi:10.1158/1538-7445.AM2015-3763

Quantitative assessments of traumatic axonal injury in human brain: concordance of microdialysis and advanced MRI.

Axonal injury is a major contributor to adverse outcomes following brain trauma. However, the extent of axonal injury cannot currently be assessed reliably in living humans. Here, we used two experimental methods with distinct noise sources and limitations in the same cohort of 15 patients with severe traumatic brain injury to assess axonal injury. One hundred kilodalton cut-off microdialysis catheters were implanted at a median time of 17 h (13-29 h) after injury in normal appearing (on computed tomography scan) frontal white matter in all patients, and samples were collected for at least 72 h. Multiple analytes, such as the metabolic markers glucose, lactate, pyruvate, glutamate and tau and amyloid-β proteins, were measured every 1-2 h in the microdialysis samples. Diffusion tensor magnetic resonance imaging scans at 3 T were performed 2-9 weeks after injury in 11 patients. Stability of diffusion tensor imaging findings was verified by repeat scans 1-3 years later in seven patients. An additional four patients were scanned only at 1-3 years after injury. Imaging abnormalities were assessed based on comparisons with five healthy control subjects for each patient, matched by age and sex (32 controls in total). No safety concerns arose during either microdialysis or scanning. We found that acute microdialysis measurements of the axonal cytoskeletal protein tau in the brain extracellular space correlated well with diffusion tensor magnetic resonance imaging-based measurements of reduced brain white matter integrity in the 1-cm radius white matter-masked region near the microdialysis catheter insertion sites. Specifically, we found a significant inverse correlation between microdialysis measured levels of tau 13-36 h after injury and anisotropy reductions in comparison with healthy controls (Spearman's r = -0.64, P = 0.006). Anisotropy reductions near microdialysis catheter insertion sites were highly correlated with reductions in multiple additional white matter regions. We interpret this result to mean that both microdialysis and diffusion tensor magnetic resonance imaging accurately reflect the same pathophysiological process: traumatic axonal injury. This cross-validation increases confidence in both methods for the clinical assessment of axonal injury. However, neither microdialysis nor diffusion tensor magnetic resonance imaging have been validated versus post-mortem histology in humans. Furthermore, future work will be required to determine the prognostic significance of these assessments of traumatic axonal injury when combined with other clinical and radiological measures.

Channel Noise from Both Slow Adaptation Currents and Fast Currents Is Required to Explain Spike-Response Variability in a Sensory Neuron

Spike-timing variability has a large effect on neural information processing. However, for many systems little is known about the noise sources causing the spike-response variability. Here we investigate potential sources of spike-response variability in auditory receptor neurons of locusts, a classic insect model system. At low-spike frequencies, our data show negative interspike-interval (ISI) correlations and ISI distributions that match the inverse Gaussian distribution. These findings can be explained by a white-noise source that interacts with an adaptation current. At higher spike frequencies, more strongly peaked distributions and positive ISI correlations appear, as expected from a canonical model of suprathreshold firing driven by temporally correlated (i.e., colored) noise. Simulations of a minimal conductance-based model of the auditory receptor neuron with stochastic ion channels exclude the delayed rectifier as a possible noise source. Our analysis suggests channel noise from an adaptation current and the receptor or sodium current as main sources for the colored and white noise, respectively. By comparing the ISI statistics with generic models, we find strong evidence for two distinct noise sources. Our approach does not involve any dendritic or somatic recordings that may harm the delicate workings of many sensory systems. It could be applied to various other types of neurons, in which channel noise dominates the fluctuations that shape the neuron's spike statistics.

The sources of jet noise: experimental evidence

The primary objective of this investigation is to determine experimentally the sources of jet mixing noise. In the present study, four different approaches are used. It is reasonable to assume that the characteristics of the noise sources are imprinted on their radiation fields. Under this assumption, it becomes possible to analyse the characteristics of the far-field sound and then infer back to the characteristics of the sources. The first approach is to make use of the spectral and directional information measured by a single microphone in the far field. A detailed analysis of a large collection of far-field noise data has been carried out. The purpose is to identify special characteristics that can be linked directly to those of the sources. The second approach is to measure the coherence of the sound field using two microphones. The autocorrelations and cross-correlations of these measurements offer not only valuable information on the spatial structure of the noise field in the radial and polar angle directions, but also on the sources inside the jet. The third approach involves measuring the correlation between turbulence fluctuations inside a jet and the radiated noise in the far field. This is the most direct and unambiguous way of identifying the sources of jet noise. In the fourth approach, a mirror microphone is used to measure the noise source distribution along the lengths of high-speed jets. Features and trends observed in noise source strength distributions are expected to shed light on the source mechanisms. It will be shown that all four types of data indicate clearly the existence of two distinct noise sources in jets. One source of noise is the fine-scale turbulence and the other source is the large turbulence structures of the jet flow. Some of the salient features of the sound field associated with the two noise sources are reported in this paper.