The Potential of Deep Learning in Underwater Wireless Sensor Networks and Noise Canceling for the Effective Monitoring of Aquatic Life.

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering-learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring.

iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG.

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.

Concept and considerations of a medical device: the active noise cancelling incubator.

Background: An increasingly 24/7 connected and urbanised world has created a silent pandemic of noise-induced hearing loss. Ensuring survival to children born (extremely) preterm is crucial. The incubator is a closed medical device, modifying the internal climate, and thus providing an environment for the child, as safe, warm, and comfortable as possible. While sound outside the incubator is managed and has decreased over the years, managing the noise inside the incubator is still a challenge. Method: Using active noise cancelling in an incubator will eliminate unwanted sounds (i.e., from the respirator and heating) inside the incubator, and by adding sophisticated algorithms, normal human speech, neonatal intensive care unit music-based therapeutic interventions, and natural sounds will be sustained for the child in the pod. Applying different methods such as active noise cancelling, motion capture, sonological engineering. and sophisticated machine learning algorithms will be implemented in the development of the incubator. Projected Results: A controlled and active sound environment in and around the incubator can in turn promote the wellbeing, neural development, and speech development of the child and minimise distress caused by unwanted noises. While developing the hardware and software pose individual challenges, it is about the system design and aspects contributing to it. On the one hand, it is crucial to measure the auditory range and frequencies in the incubator, as well as the predictable sounds that will have to be played back into the environment. On the other, there are many technical issues that have to be addressed when it comes to algorithms, datasets, delay, microphone technology, transducers, convergence, tracking, impulse control and noise rejection, noise mitigation stability, detection, polarity, and performance. Conclusion: Solving a complex problem like this, however, requires a de-disciplinary approach, where each discipline will realise its own shortcomings and boundaries, and in turn will allow for innovations and new avenues. Technical developments used for building the active noise cancellation-incubator have the potential to contribute to improved care solutions for patients, both infants and adults.Code available at: 10.3389/fped.2023.1187815.

Association between Third Mobile Window Disorders and Symptom Reduction Using a Noise Cancelling Device: Inverse Tullio Phenomena.

Identifying a vestibular source of pathology in patients complaining of post-traumatic brain injury (TBI) dizziness can be difficult. We describe a possible new method utilizing a reduction in post-TBI symptoms (including dizziness) with the use of a noise cancellation device (NCD). This retrospective case series included patients with TBI and dizziness presenting to a binocular vision specialty clinic, who were diagnosed with a vertical heterophoria (VH). If they did not respond adequately to microprism lenses and/or if they experienced hyperacusis, they were evaluated with an NCD. If there was marked reduction in TBI symptoms (including dizziness), the patients were referred to a neuro-otologist for vestibular diagnostic evaluation and treatment. Fourteen patients were identified and found to have abnormalities on vestibular testing consistent with third mobile window disorder (TMWD). All were treated with a 6-week medical protocol (diuretics, no straining, low sodium/no caffeine diet). Five responded positively, requiring no further treatment. Nine required surgical intervention and responded positively. In conclusion, in 14 patients with post-concussive dizziness and VH, a positive response to NCD was associated with abnormal vestibular testing, a diagnosis of TMWD, and symptom reduction/resolution with a medical or surgical approach. The removal of sound resulting in reduction or resolution of vestibular symptoms represents an inverse Tullio phenomenon.

Improved Biomagnetic Signal-To-Noise Ratio and Source Localization Using Optically Pumped Magnetometers with Synthetic Gradiometers.

Optically pumped magnetometers (OPMs) can capture brain activity but are susceptible to magnetic noise. The objective of this study was to evaluate a novel methodology used to reduce magnetic noise in OPM measurements. A portable magnetoencephalography (MEG) prototype was developed with OPMs. The OPMs were divided into primary sensors and reference sensors. For each primary sensor, a synthetic gradiometer (SG) was constructed by computing a secondary sensor that simulated noise with signals from the reference sensors. MEG data from a phantom with known source signals and six human participants were used to assess the efficacy of the SGs. Magnetic noise in the OPM data appeared predominantly in a low frequency range (<4 Hz) and varied among OPMs. The SGs significantly reduced magnetic noise (p < 0.01), enhanced the signal-to-noise ratio (SNR) (p < 0.001) and improved the accuracy of source localization (p < 0.02). The SGs precisely revealed movement-evoked magnetic fields in MEG data recorded from human participants. SGs provided an effective method to enhance SNR and improve the accuracy of source localization by suppressing noise. Software-simulated SGs may provide new opportunities regarding the use of OPM measurements in various clinical and research applications, especially those in which movement is relevant.

Ultrasensitive bacterial sensing using a disposable all-in-one amperometric platform with self-noise cancellation.

The early detection of very low bacterial concentrations is key to minimize the healthcare and safety issues associated with microbial infections, food poisoning or water pollution. In amperometric integrated circuits for electrochemical sensors, flicker noise is still the main bottleneck to achieve ultrasensitive detection with small footprint, cost-effective and ultra-low power instrumentation. Current strategies rely on autozeroing or chopper stabilization causing negative impacts on chip size and power consumption. This work presents a 27-µW potentiostatic-amperometric Delta-Sigma modulator able to cancel its own flicker noise and provide a 4-fold improvement in the limit of detection. The 2.3-mm2 all-in-one CMOS integrated circuit is glued to an inkjet-printed electrochemical sensor. Measurements show that the limit of detection is 15 pArms, the extended dynamic range reaches 110 dB and linearity is R2 = 0.998. The disposable device is able to detect, in less than 1h, live bacterial concentrations as low as 102 CFU/mL from a 50-µL droplet sample, which is equivalent to 5 microorganisms.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors.

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. METHODS: The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. RESULTS: MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. CONCLUSIONS: The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease.

Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.

Objective Signal Analysis for Investigating Feasibility of Active Noise Cancellation in Hearing Screening.

With the development of active noise cancellation (ANC) technology, ANC has been used to mitigate the effects of environmental noise on audiometric results. However, objective evaluation methods supporting the accuracy of audiometry for ANC exposure to different levels of noise have not been reported. Accordingly, the audio characteristics of three different ANC headphone models were quantified under different noise conditions and the feasibility of ANC in noisy environments was investigated. Steady (pink noise) and non-steady noise (cafeteria babble noise) were used to simulate noisy environments. We compared the integrity of pure-tone signals obtained from three different ANC headphone models after processing under different noise scenarios and analyzed the degree of ANC signal correlation based on the Pearson correlation coefficient compared to pure-tone signals in quiet. The objective signal correlation results were compared with audiometric screening results to confirm the correspondence. Results revealed that ANC helped mitigate the effects of environmental noise on the measured signal and the combined ANC headset model retained the highest signal integrity. The degree of signal correlation was used as a confidence indicator for the accuracy of hearing screening in noise results. It was found that the ANC technique can be further improved for more complex noisy environments.

Separation of Spacecraft Noise From Geomagnetic Field Observations Through Density-Based Cluster Analysis and Compressive Sensing.

The use of magnetometers for space exploration is inhibited by magnetic noise generated by spacecraft electrical systems. Mechanical booms are traditionally used to extend magnetometers away from noise sources. If a spacecraft is equipped with multiple magnetometers, signal processing algorithms can be used to compare magnetometer measurements and remove stray magnetic noise signals. We propose the use of density-based cluster analysis to identify spacecraft noise signals and compressive sensing to separate spacecraft noise from geomagnetic field data. This method assumes no prior knowledge of the number, location, or amplitude of noise signals, but assumes that they have minimal overlapping spectral properties. We demonstrate the validity of this algorithm by separating high latitude magnetic perturbations recorded by the low-Earth orbiting satellite, SWARM, from noise signals in simulation and in a laboratory experiment using a mock CubeSat apparatus. In the case of more noise sources than magnetometers, this problem is an instance of underdetermined blind source separation (UBSS). This work presents a UBSS signal processing algorithm to remove spacecraft noise and minimize the need for a mechanical boom.

Coherence Function and Adaptive Noise Cancellation Performance of an Acoustic Sensor System for Use in Detecting Coronary Artery Disease.

Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean squares (WLMS) algorithm on a stethoscope system for use in detecting coronary artery disease in the presence of background noise. Each stethoscope is equipped with two microphones: one used to detect heart signals and one used to detect background noise. The WLMS method was used for four different sources of background noise whilst measuring a heartbeat, including a single tone, multiple tones, hospital/clinic noise, and breathing noise. The magnitude-squared coherence between both microphones was unity for the tone scenarios, resulting in complete attenuation. For the other background noise sources, a less-than-unity magnitude-squared coherence resulted in minor and no attenuation. Thus, the coherence function is a tool that can be used to predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques, such as WLMS, as presented in this article.

Spatial and Frequency Specific Artifact Reduction in Optically Pumped Magnetometer Recordings.

BACKGROUND: Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) opens up new opportunities for brain research. However, OPM recordings are associated with artifacts. We describe a new artifact reduction method, frequency specific signal space classification (FSSSC), to improve the signal-to-noise ratio of OPM recordings. METHODS: FSSSC was based on time-frequency analysis and signal space classification (SSC). SSC was accomplished by computing the orthogonality of the brain signal and artifact. A dipole phantom was used to determine if the method could remove artifacts and improve accuracy of source localization. Auditory evoked magnetic fields (AEFs) from human subjects were used to assess the usefulness of artifact reduction in the study of brain function because bilateral AEFs have proven a good benchmark for testing new methods. OPM data from empty room recordings were used to estimate magnetic artifacts. The effectiveness of FSSSC was assessed in waveforms, spectrograms, and covariance domains. RESULTS: MEG recordings from phantom tests show that FSSSC can remove artifacts, normalize waveforms, and significantly improve source localization accuracy. MEG signals from human subjects show that FSSC can reveal auditory evoked magnetic responses overshadowed and distorted by artifacts. The present study demonstrates FSSSC is effective at removing artifacts in OPM recordings. This can facilitate the analyses of waveforms, spectrograms, and covariance. The accuracy of source localization of OPM recordings can be significantly improved by FSSSC. CONCLUSIONS: Brain responses distorted by artifacts can be restored. The results of the present study strongly support that artifact reduction is very important in order for OPMs to become a viable alternative to conventional MEG.

State-of-the-Art Light to Digital Converter Circuits Applicable in Non-Invasive Health Monitoring Devices to Combat COVID-19 and Other Respiratory Illnesses: A Review.

In the past few years, a tremendous advancement in the outcome of biomedical circuits and systems has been reported. Unfortunately, at the time of the sudden outbreak of COVID-19, the electronic engineering researchers felt dearth on their side to combat the pandemic, as no such immediate cutting-edge solutions were ready to recognize the virus with some standard and smart electronic devices. Likely, in this paper, a detailed comparative and comprehensive study on circuit architectures of the biomedical devices is presented. Mostly, this study relates the industry standard circuit schemes applicable in non-invasive health monitoring to combat respiratory illnesses. The trending circuit architectural schemes casted-off to tapeout non-invasive health-care devices available in the past literature are meticulously and broadly discussed in this study. Further, the comprehensive comparison of the state of art of the device performance in terms of supply voltage, chip area, sensitivity, dynamic range, etc. is also shown in this paper. The inclusive design processes of the health monitoring devices from Lab to Industry is thoroughly discussed for the readers. The authors think, that this critical review summarising all the trending and most cited health-care devices in a single paper will alternately help the industrialists to adapt and modify the circuit architectures of the health monitoring devices more precisely and straightforwardly. Finally, the demand for health monitoring devices particularly responsible to detect respiratory illnesses, measuring blood pressure and heart-rate is growing widely in the market after the the incident of COVID-19 and other respiratory diseases.

HearCough: Enabling continuous cough event detection on edge computing hearables.

Cough event detection is the foundation of any measurement associated with cough, one of the primary symptoms of pulmonary illnesses. This paper proposes HearCough, which enables continuous cough event detection on edge computing hearables, by leveraging always-on active noise cancellation (ANC) microphones in commodity hearables. Specifically, we proposed a lightweight end-to-end neural network model - Tiny-COUNET and its transfer learning based traning method. When evaluated on our acted cough event dataset, Tiny-COUNET achieved equivalent detection performance but required significantly less computational resources and storage space than cutting-edge cough event detection methods. Then we implemented HearCough by quantifying and deploying the pre-trained Tiny-COUNET to a popular micro-controller in consumer hearables. Lastly, we evaluated that HearCough is effective and reliable for continuous cough event detection through a field study with 8 patients. HearCough achieved 2 Hz cough event detection with an accuracy of 90.0% and an F1-score of 89.5% by consuming an additional 5.2 mW power. We envision HearCough as a low-cost add-on for future hearables to enable continuous cough detection and pulmonary health monitoring.

Effects of an Active Noise Control Technology Applied to Earphones on Preferred Listening Levels in Noisy Environments.

BACKGROUND AND OBJECTIVES: The harmful effects of frequent exposure to loud sounds through portable music players (PMPs) in combination with earphones have been suggested to result in a high prevalence of recreational noise-induced hearing loss among children, adolescents, and young adults. The present study aimed to evaluate the effects of an active noise control technology applied to earphones on the preferred listening levels (PLLs) while listening to music in the presence of background noise. SUBJECTS AND METHODS: Twenty-three adults between 20 and 40 years with normal hearing were recruited for this study. PLLs for listening to pop-rock and classical music were measured in the participants' ear canal with a commercially available PMP for four earphone/headphone configurations in quiet and noisy conditions. Ear canal insertion loss was measured in open ear conditions as well as earphone/headphone conditions. RESULTS: The average PLL while using earphones and headphones exceeded 85 dBA corresponding to the sound level to induce hearing damage, but in the case of canal earphones with noise cancelling (NC), it was below 75 dBA, corresponding to potentially harmful levels. The background subway noise significantly increased the PLL measured in quiet conditions using any four earphone/headphone types except canal earphones with NC. Canal earphones with NC showed the lowest PLLs compared with participants' average PLLs using the other three earphone/headphone types. CONCLUSIONS: To minimize recreational noise exposure at the risk of PMP use, the use of earphones with NC is recommended in noisy environments.

A Wideband Noise and Harmonic Distortion Canceling Low-Noise Amplifier for High-Frequency Ultrasound Transducers.

This paper presents a wideband low-noise amplifier (LNA) front-end with noise and distortion cancellation for high-frequency ultrasound transducers. The LNA employs a resistive shunt-feedback structure with a feedforward noise-canceling technique to accomplish both wideband impedance matching and low noise performance. A complementary CMOS topology was also developed to cancel out the second-order harmonic distortion and enhance the amplifier linearity. A high-frequency ultrasound (HFUS) and photoacoustic (PA) imaging front-end, including the proposed LNA and a variable gain amplifier (VGA), was designed and fabricated in a 180 nm CMOS process. At 80 MHz, the front-end achieves an input-referred noise density of 1.36 nV/sqrt (Hz), an input return loss (S11) of better than -16 dB, a voltage gain of 37 dB, and a total harmonic distortion (THD) of -55 dBc while dissipating a power of 37 mW, leading to a noise efficiency factor (NEF) of 2.66.

Adaptive Removal of high-density salt-and-pepper noise (ARSPN) for robust ROI detection used in watermarking of MRI images of the brain.

This paper presents a novel window-based method to remove high-density salt-and-pepper noise for optimal ROI (Region Of Interest) detection of brain MRI (Magnetic Resonance Imaging) images. The output of this system is used in watermarking and extracting hidden data in this type of image. In this method, for each pixel of the noisy input image, an adaptive n × n window is considered in the neighborhood of that pixel. If they are not noisy, the pixels of this window are weighted according to their distance from the desired pixel, and the weighted sum of the neighboring pixels is averaged. Then the noisy pixel replaces with the resulting value. This paper consists of three main sections: ROI detection, noise removal block, and evaluation of the proposed method against different densities of salt-and-pepper noise in the range of 1%-98%. ROI obtained by this method is the same before and after the noise. The final image has an acceptable PSNR (Peak Signal-to-Noise Ratio) for noise with various densities. Based on the experimental results obtained by the high efficient proposed noise removal method using 208 images from seven Databases (DBs), the maximum value is 61.7% for the 1% noise density and 26.4% for the 98% noise density.

Noise Cancellation of Helicopter Blade Deformations Measurement by Fiber Bragg Gratings.

The work presents data treatment methods aimed at eliminating the noise in the strain sensor data induced by vibrations of the helicopter blade in flight conditions. The methods can be applied in order to enhance the metrological performance of the helicopter weight estimation system based on the deformation measurement of the main rotor blades. The experimental setup included a composite plate fixed to the vibrating stand on the one end, with six fiber-optic strain sensors attached to its surface. In this work, the procedure of the optimal linear smoothing (POLS) and 3D-invariant methods were used to obtain monotone calibration curves for each detector, thereby making it possible to distinguish the increase of load applied to the free end of the plate with an increment of 10 g. The second method associated with 3D invariants took into account 13 quantitative parameters defined as the combination of different moments and their intercorrelations up to the fourth-order inclusive. These 13 parameters allowed the calculation of the 3D surface that can serve as a specific fingerprint, differentiating one set of initial data from another one. The combination of the two data treatment methods used in this work can be applied successfully in a wide variety of applications.

On the importance of interaural noise coherence and the medial olivocochlear reflex for binaural unmasking in free-field listening.

For speech in competition with a noise source in the free field, normal-hearing (NH) listeners recognize speech better when listening binaurally than when listening monaurally with the ear that has the better acoustic signal-to-noise ratio (SNR). This benefit from listening binaurally is known as binaural unmasking and indicates that the brain combines information from the two ears to improve intelligibility. Here, we address three questions pertaining to binaural unmasking for NH listeners. First, we investigate if binaural unmasking results from combining the speech and/or the noise from the two ears. In a simulated acoustic free field with speech and noise sources at 0° and 270°azimuth, respectively, we found comparable unmasking regardless of whether the speech was present or absent in the ear with the worse SNR. This indicates that binaural unmasking probably involves combining only the noise at the two ears. Second, we investigate if having binaurally coherent location cues for the noise signal is sufficient for binaural unmasking to occur. We found no unmasking when location cues were coherent but noise signals were generated incoherent or were processed unilaterally through a hearing aid with linear, minimal amplification. This indicates that binaural unmasking requires interaurally coherent noise signals, source location cues, and processing. Third, we investigate if the hypothesized antimasking benefits of the medial olivocochlear reflex (MOCR) contribute to binaural unmasking. We found comparable unmasking regardless of whether speech tokens (words) were sufficiently delayed from the noise onset to fully activate the MOCR or not. Moreover, unmasking was absent when the noise was binaurally incoherent whereas the physiological antimasking effects of the MOCR are similar for coherent and incoherent noises. This indicates that the MOCR is unlikely involved in binaural unmasking.

Lung mediated auditory contrast enhancement improves the Signal-to-noise ratio for communication in frogs.

Environmental noise is a major source of selection on animal sensory and communication systems. The acoustic signals of other animals represent particularly potent sources of noise for chorusing insects, frogs, and birds, which contend with a multi-species analog of the human "cocktail party problem" (i.e., our difficulty following speech in crowds). However, current knowledge of the diverse adaptations that function to solve noise problems in nonhuman animals remains limited. Here, we show that a lung-to-ear sound transmission pathway in frogs serves a heretofore unknown noise-control function in vertebrate hearing and sound communication. Inflated lungs improve the signal-to-noise ratio for communication by enhancing the spectral contrast in received vocalizations in ways analogous to signal processing algorithms used in hearing aids and cochlear implants. Laser vibrometry revealed that the resonance of inflated lungs selectively reduces the tympanum's sensitivity to frequencies between the two spectral peaks present in conspecific mating calls. Social network analysis of continent-scale citizen science data on frog calling behavior revealed that the calls of other frog species in multi-species choruses can be a prominent source of environmental noise attenuated by the lungs. Physiological modeling of peripheral frequency tuning indicated that inflated lungs could reduce both auditory masking and suppression of neural responses to mating calls by environmental noise. Together, these data suggest an ancient adaptation for detecting sound via the lungs has been evolutionarily co-opted to create auditory contrast enhancement that contributes to solving a multi-species cocktail party problem.