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Gil Weinberg

Publications and source records attributed to Gil Weinberg.

At least 19 recordsLinked to original sources

Beyond memory-effect matrix-based imaging in scattering media by acousto-optic gating

Imaging inside scattering media at optical resolution is a longstanding challenge affecting multiple fields, from bio-medicine to astronomy. In recent years, several groundbreaking techniques for imaging inside scattering media, in particular scattering-matrix based approaches, have shown great promise, but usually suffer from a restricted field of view (FOV) due to their reliance on the optical 'memory-effect'. Here, we demonstrate that by combining acousto-optic spatial-gating with state-of-the-art matrix-based imaging techniques, diffraction-limited imaging beyond the optical memory-effect can be achieved in a robust fashion. Specifically, we show that this can be achieved by computational processing of scattered light fields captured under scanned acousto-optic modulation. The approach can be directly utilized whenever the ultrasound focus size is of the order of the memory-effect range, independently of the scattering angle.

physics.optics

Coherent Perfect Absorption of Arbitrary Wavefronts at an Exceptional Point

A Coherent Perfect Absorber (CPA) exploits the interferometric nature of light to deposit all of a light field's incident energy into an otherwise weakly absorbing sample. The downside of this concept is that the necessary destructive interference in CPAs gets easily destroyed both by spectrally or spatially detuning the incoming light field. Each of these two limitations has recently been overcome by insights from exceptional-point physics and by using a degenerate cavity, respectively. Here, we show how these two concepts can be combined into a new type of cavity design, which allows broadband exceptional-point absorption of arbitrary wavefronts. We present two possible implementations of such a Massively Degenerate Exceptional-Point absorber and compare analytical results with numerical simulations.

physics.optics

Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle

Conventional fiber-bundle-based endoscopes allow minimally invasive imaging through flexible multi-core fiber (MCF) bundles by placing a miniature lens at the distal tip and using each core as an imaging pixel. In recent years, lensless imaging through MCFs was made possible by correcting the core-to-core phase distortions pre-measured in a calibration procedure. However, temporally varying wavefront distortions, for instance, due to dynamic fiber bending, pose a challenge for such approaches. Here, we demonstrate a coherent lensless imaging technique based on intensity-only measurements insensitive to core-to-core phase distortions. We leverage a ptychographic reconstruction algorithm to retrieve the phase and amplitude profiles of reflective objects placed at a distance from the fiber tip, using as input a set of diffracted intensity patterns reflected from the object when the illumination is scanned over the MCF cores. Our approach thus utilizes an acquisition process equivalent to confocal microendoscopy, only replacing the single detector with a camera.

physics.optics

Noninvasive megapixel fluorescence microscopy through scattering layers by a virtual reflection-matrix

Optical-resolution fluorescence imaging through and within complex samples presents a significant challenge due to random light scattering, with substantial implications across multiple fields. While significant advancements in coherent imaging through severe multiple scattering have been recently introduced by reflection-matrix processing, approaches that tackle scattering in incoherent fluorescence imaging have been limited to sparse targets, require high-resolution control of the illumination or detection wavefronts, or a very large number of measurements. Here, we present an approach that allows direct application of well-established reflection-matrix techniques to scattering compensation in incoherent fluorescence imaging. We experimentally demonstrate that a small number of conventional widefield fluorescence-microscope images acquired under unknown random illuminations can effectively construct a fluorescence-based virtual reflection matrix. This matrix, when processed by conventional matrix-based scattering compensation algorithms, allows reconstructing megapixel-scale fluorescence images, without requiring the use of spatial-light modulators (SLMs) or computationally-intensive processing.

physics.optics

Image scanning lensless fiber-bundle endomicroscopy

Fiber-based confocal endomicroscopy has shown great promise for minimally-invasive deep-tissue imaging. Despite its advantages, confocal fiber-bundle endoscopy inherently suffers from undersampling due to the spacing between fiber cores, and low collection efficiency when the target is not in proximity to the distal fiber facet. Here, we demonstrate an adaptation of image-scanning microscopy (ISM) to lensless fiber bundle endoscopy, doubling the spatial sampling frequency and significantly improving collection efficiency. Our approach only requires replacing the confocal detector with a camera. It improves the spatial resolution for targets placed at a distance from the fiber tip, and addresses the fundamental challenge of aliasing/pixelization artifacts.

physics.optics

K-space interpretation of image-scanning-microscopy

In recent years, image-scanning microscopy (ISM, also termed pixel-reassignment microscopy) has emerged as a technique that improves the resolution and signal-to-noise compared to confocal and widefield microscopy by employing a detector array at the image plane of a confocal laser scanning microscope. Here, we present a k-space analysis of coherent ISM, showing that ISM is equivalent to spotlight synthetic-aperture radar (SAR) and analogous to oblique-illumination microscopy. This insight indicates that ISM can be performed with a single detector placed in the k-space of the sample, which we numerically demonstrate.

physics.optics

Massively degenerate coherent perfect absorber for arbitrary wavefronts

One of the key insights in the emerging field of non-Hermitian photonics is that well-established concepts like the laser can be operated in reverse to realize a 'coherent perfect absorber' (CPA). While conceptually appealing, such CPAs are limited so far to a single, judiciously shaped wavefront or 'mode'. Here, we demonstrate how this limitation can be overcome by time-reversing a 'degenerate cavity laser', based on a unique cavity that self-images any incident light-field onto itself. Placing a weak, critically-coupled absorber into this cavity, we demonstrate that any incoming wavefront, even a complex and dynamically-varying speckle pattern, is absorbed with close to perfect efficiency in a massively parallel interference process. Moreover, the coherent nature of multi-mode absorption allows us to tune the degree of absorption over a wide range. These characteristics open-up interesting new possibilities for applications in light-harvesting, energy delivery, light control, and imaging.

physics.optics

Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning

Lyric generation is a popular sub-field of natural language generation that has seen growth in recent years. Pop lyrics are of unique interest due to the genre's unique style and content, in addition to the high level of collaboration that goes on behind the scenes in the professional pop songwriting process. In this paper, we present a collaborative line-level lyric generation system that utilizes transfer-learning via the T5 transformer model, which, till date, has not been used to generate pop lyrics. By working and communicating directly with professional songwriters, we develop a model that is able to learn lyrical and stylistic tasks like rhyming, matching line beat requirements, and ending lines with specific target words. Our approach compares favorably to existing methods for multiple datasets and yields positive results from our online studies and interviews with industry songwriters.

cs.CL

Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning

The task of classifying emotions within a musical track has received widespread attention within the Music Information Retrieval (MIR) community. Music emotion recognition has traditionally relied on the use of acoustic features, verbal features, and metadata-based filtering. The role of musical prosody remains under-explored despite several studies demonstrating a strong connection between prosody and emotion. In this study, we restrict the input of traditional machine learning algorithms to the features of musical prosody. Furthermore, our proposed approach builds upon the prior by classifying emotions under an expanded emotional taxonomy, using the Geneva Wheel of Emotion. We utilize a methodology for individual data collection from vocalists, and personal ground truth labeling by the artist themselves. We found that traditional machine learning algorithms when limited to the features of musical prosody (1) achieve high accuracies for a single singer, (2) maintain high accuracy when the dataset is expanded to multiple singers, and (3) achieve high accuracies when trained on a reduced subset of the total features.

cs.SD

Acousto-optic Ptychography

Acousto-optic imaging (AOI) enables optical-contrast imaging deep inside scattering samples via localized ultrasound-modulation of scattered light. While AOI allows optical investigations at depths, its imaging resolution is inherently limited by the ultrasound wavelength, prohibiting microscopic investigations. Here, we propose a novel computational imaging approach that allows to achieve optical diffraction-limited imaging using a conventional AOI system. We achieve this by extracting diffraction-limited imaging information from 'memory-effect' speckle-correlations in the conventionally detected ultrasound-modulated scattered-light fields. Specifically, we identify that since speckle correlations allow to estimate the Fourier-magnitude of the field inside the ultrasound focus, scanning the ultrasound focus enables robust diffraction-limited reconstruction of extended objects using ptychography, i.e. we exploit the ultrasound focus as the scanned spatial-gate 'probe' required for ptychographic phase-retrieval. Moreover, we exploit the short speckle decorrelation-time in dynamic media, which is usually considered a hurdle for wavefront-shaping based approaches, for improved ptychographic reconstruction. We experimentally demonstrate non-invasive imaging of targets that extend well beyond the memory-effect range, with a 40-times resolution improvement over conventional AOI, surpassing the performance of state-of-the-art approaches.

physics.optics

Shimon the Robot Film Composer and DeepScore: An LSTM for Generation of Film Scores based on Visual Analysis

Composing for a film requires developing an understanding of the film, its characters and the film aesthetic choices made by the director. We propose using existing visual analysis systems as a core technology for film music generation. We extract film features including main characters and their emotions to develop a computer understanding of the film's narrative arc. This arc is combined with visually analyzed director aesthetic choices including pacing and levels of movement. Two systems are presented, the first using a robotic film composer and marimbist to generate film scores in real-time performance. The second software-based system builds on the results from the robot film composer to create narrative driven film scores.

cs.CV

Emotional Musical Prosody: Validated Vocal Dataset for Human Robot Interaction

Human collaboration with robotics is dependant on the development of a relationship between human and robot, without which performance and utilization can decrease. Emotion and personality conveyance has been shown to enhance robotic collaborations, with improved human-robot relationships and increased trust. One under-explored way for an artificial agent to convey emotions is through non-linguistic musical prosody. In this work we present a new 4.2 hour dataset of improvised emotional vocal phrases based on the Geneva Emotion Wheel. This dataset has been validated through extensive listening tests and shows promising preliminary results for use in generative systems.

cs.RO

Shimon the Rapper: A Real-Time System for Human-Robot Interactive Rap Battles

We present a system for real-time lyrical improvisation between a human and a robot in the style of hip hop. Our system takes vocal input from a human rapper, analyzes the semantic meaning, and generates a response that is rapped back by a robot over a musical groove. Previous work with real-time interactive music systems has largely focused on instrumental output, and vocal interactions with robots have been explored, but not in a musical context. Our generative system includes custom methods for censorship, voice, rhythm, rhyming and a novel deep learning pipeline based on phoneme embeddings. The rap performances are accompanied by synchronized robotic gestures and mouth movements. Key technical challenges that were overcome in the system are developing rhymes, performing with low-latency and dataset censorship. We evaluated several aspects of the system through a survey of videos and sample text output. Analysis of comments showed that the overall perception of the system was positive. The model trained on our hip hop dataset was rated significantly higher than our metal dataset in coherence, rhyme quality, and enjoyment. Participants preferred outputs generated by a given input phrase over outputs generated from unknown keywords, indicating that the system successfully relates its output to its input.

cs.AI

Emotional Musical Prosody for the Enhancement of Trust in Robotic Arm Communication

As robotic arms become prevalent in industry it is crucial to improve levels of trust from human collaborators. Low levels of trust in human-robot interaction can reduce overall performance and prevent full robot utilization. We investigated the potential benefits of using emotional musical prosody to allow the robot to respond emotionally to the user's actions. We tested participants' responses to interacting with a virtual robot arm that acted as a decision agent, helping participants select the next number in a sequence. We compared results from three versions of the application in a between-group experiment, where the robot had different emotional reactions to the user's input depending on whether the user agreed with the robot and whether the user's choice was correct. In all versions, the robot reacted with emotional gestures. One version used prosody-based emotional audio phrases selected from our dataset of singer improvisations, the second version used audio consisting of a single pitch randomly assigned to each emotion, and the final version used no audio, only gestures. Our results showed no significant difference for the percentage of times users from each group agreed with the robot, and no difference between user's agreement with the robot after it made a mistake. However, participants also took a trust survey following the interaction, and we found that the reported trust ratings of the musical prosody group were significantly higher than both the single-pitch and no audio groups.

cs.RO

A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction

As emotion plays a growing role in robotic research it is crucial to develop methods to analyze and compare among the wide range of approaches. To this end we present a survey of 1427 IEEE and ACM publications that include robotics and emotion. This includes broad categorizations of trends in emotion input analysis, robot emotional expression, studies of emotional interaction and models for internal processing. We then focus on 232 papers that present internal processing of emotion, such as using a human's emotion for better interaction or turning environmental stimuli into an emotional drive for robotic path planning. We conducted constant comparison analysis of the 232 papers and arrived at three broad categorization metrics; emotional intelligence, emotional model and implementation, each including two or three subcategories. The subcategories address the algorithm used, emotional mapping, history, the emotional model, emotional categories, the role of emotion, the purpose of emotion and the platform. Our results show a diverse field of study, largely divided by the role of emotion in the system, either for improved interaction, or improved robotic performance. We also present multiple future opportunities for research and describe intrinsic challenges common in all publications.

cs.RO

Mechatronics-Driven Musical Expressivity for Robotic Percussionists

Musical expressivity is an important aspect of musical performance for humans as well as robotic musicians. We present a novel mechatronics-driven implementation of Brushless Direct Current (BLDC) motors in a robotic marimba player, named Shimon, designed to improve speed, dynamic range (loudness), and ultimately perceived musical expressivity in comparison to state-of-the-art robotic percussionist actuators. In an objective test of dynamic range, we find that our implementation provides wider and more consistent dynamic range response in comparison with solenoid-based robotic percussionists. Our implementation also outperforms both solenoid and human marimba players in striking speed. In a subjective listening test measuring musical expressivity, our system performs significantly better than a solenoid-based system and is statistically indistinguishable from human performers.

cs.RO

100,000 frames-per-second compressive imaging with a conventional rolling-shutter camera by random point-spread-function engineering

We demonstrate an approach that allows taking videos at very high-speeds of over 100,000 frames per second (fps) by exploiting the fast sampling rate of the standard rolling-shutter readout mechanism, common to most conventional sensors, and a compressive-sampling acquisition scheme. Our approach is directly applied to a conventional imaging system by the simple addition of a diffuser to the pupil plane, randomly encoding the entire field-of-view to each camera row, while maintaining diffraction-limited resolution. A short video is reconstructed from a single camera frame via a compressed-sensing reconstruction algorithm, exploiting inherent sparsity of the imaged scene.

eess.IV

Establishing Human-Robot Trust through Music-Driven Robotic Emotion Prosody and Gesture

As human-robot collaboration opportunities continue to expand, trust becomes ever more important for full engagement and utilization of robots. Affective trust, built on emotional relationship and interpersonal bonds is particularly critical as it is more resilient to mistakes and increases the willingness to collaborate. In this paper we present a novel model built on music-driven emotional prosody and gestures that encourages the perception of a robotic identity, designed to avoid uncanny valley. Symbolic musical phrases were generated and tagged with emotional information by human musicians. These phrases controlled a synthesis engine playing back pre-rendered audio samples generated through interpolation of phonemes and electronic instruments. Gestures were also driven by the symbolic phrases, encoding the emotion from the musical phrase to low degree-of-freedom movements. Through a user study we showed that our system was able to accurately portray a range of emotions to the user. We also showed with a significant result that our non-linguistic audio generation achieved an 8% higher mean of average trust than using a state-of-the-art text-to-speech system.

cs.HC