Instrument Identification Research Articles

Matched wavelets for musical signal processing using evolutionary algorithms

The non-stationary nature of musical signals presents challenges for conventional signal analysis methods. Wavelet transforms offer a powerful tool for capturing both temporal and frequency information simultaneously. This study introduces a novel approach to enhance wavelet analysis in music processing by utilizing matched wavelets optimized through evolutionary algorithms, specifically tailored for musical signals within the context of Indian Classical Music (ICM). Various evolutionary algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) were investigated. The proposed method optimizes wavelet parameters to match the characteristics of a given signal resulting in a customized CWT filter bank. The scalogram accurately highlights the fundamental frequency and its harmonic components. The efficacy of this approach is validated through comparisons with established techniques such as Short-Time Fourier Transform (STFT) and S-Transform. The designed wavelets achieve a high correlation coefficient in signal reconstruction, outperforming standard continuous wavelets. The customized wavelets not only facilitate the detailed analysis of signal components but also ensure robust signal reconstruction. The use of matched wavelets in feature extraction has shown promising results in tasks such as swara recognition and instrument identification in monophonic music.

Impact of Financial Instrument Identification and Reclassification on Enterprises

Impact of Financial Instrument Identification and Reclassification on Enterprises

Evaluating Feature Selection Algorithms for Machine Learning-Based Musical Instrument Identification in Monophonic Recordings

Musical instrument identification (MII) research has been studied as a subfield of the Music Information Retrieval (MIR) field. Conventional MII models are developed based on hierarchical models representing musical instrument families. However, for MII models to be used in the field of music production, they should be developed based on the arrangement-based functions of instruments in musical styles rather than these hierarchical models. This study investigates how the performance of machine learning based classification algorithms for Guitar, Bass guitar and Drum classes changes with different feature selection algorithms, considering a popular music production scenario. To determine the effect of feature statistics on model performance, Minimum Redundancy Maximum Relevance (mRMR), Chi-sqaure (Chi2), ReliefF, Analysis of Variance (ANOVA) and Kruskal Wallis feature selection algorithms were used. In the end, the neural network algorithm with wide hyperparameters (WNN) achieved the best classification accuracy (91.4%) when using the first 20 statistics suggested by the mRMR and ReliefF feature selection algorithms.

Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend the ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low-level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tools and the left-handed tools. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. On the other hand, for gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

Observed rates of surgical instrument errors point to visualization tasks as being a critically vulnerable point in sterile processing and a significant cause of lost chargeable OR minutes

BackgroundThe reporting of surgical instrument errors historically relies on cumbersome, non-automated, human-dependent, data entry into a computer database that is not integrated into the electronic medical record. The limitations of these reporting systems make it difficult to accurately estimate the negative impact of surgical instrument errors on operating room efficiencies. We set out to determine the impact of surgical instrument errors on a two-hospital healthcare campus using independent observers trained in the identification of Surgical Instrument Errors.MethodsThis study was conducted in the 7 pediatric ORs at an academic healthcare campus. Direct observations were conducted over the summer of 2021 in the 7 pediatric ORs by 24 trained student observers during elective OR days. Surgical service line, error type, case type (inpatient or outpatient), and associated length of delay were recorded.ResultsThere were 236 observed errors affecting 147 individual surgical cases. The three most common errors were Missing+ (n = 160), Broken/poorly functioning instruments (n = 44), and Tray+ (n = 13). Errors arising from failures in visualization (i.e. inspection, identification, function) accounted for 88.6% of all errors (Missing+/Broken/Bioburden). Significantly more inpatient cases (42.73%) had errors than outpatient cases (22.32%) (p = 0.0129). For cases in which data was collected on whether an error caused a delay (103), over 50% of both IP and OP cases experienced a delay. The average length of delays per case was 10.16 min. The annual lost charges in dollars for surgical instrument associated delays in chargeable minutes was estimated to be between $6,751,058.06 and $9,421,590.11.ConclusionsThese data indicate that elimination of surgical instrument errors should be a major target of waste reduction. Most observed errors (88.6%) have to do with failures in the visualization required to identify, determine functionality, detect the presence of bioburden, and assemble instruments into the correct trays. To reduce these errors and associated waste, technological advances in instrument identification, inspection, and assembly will need to be made and applied to the process of sterile processing.

Objective and Subjective Assessment of Music Perception and Musical Experiences in Young Cochlear Implant Users.

For many individuals, music has a significant impact on the quality and enjoyability of life. Cochlear implant (CI) users must cope with the constraints that the CI imposes on music perception. Here, we assessed the musical experiences of young CI users and age-matched controls with normal hearing (NH). CI users and NH peers were divided into subgroups according to age: children and adolescents. Participants were tested on their ability to recognize vocal and instrumental music and instruments. A music questionnaire for pediatric populations (MuQPP) was also used. CI users and NH peers identified a similar percentage of vocal music. CI users were significantly worse at recognizing instruments (p < 0.05) and instrumental music (p < 0.05). CI users scored similarly to NH peers on the MuQPP, except for the musical frequency domain, where CI users in the children subgroup scored higher than their NH peers (p = 0.009). For CI users in the children subgroup, the identification of instrumental music was positively correlated with music importance (p = 0.029). Young CI users have significant deficits in some aspects of music perception (instrumental music and instrument identification) but have similar scores to NH peers in terms of interest in music, frequency of music exposure, and importance of music.

Surgical Instrument Recognition Based on Improved YOLOv5

Recognition of surgical instruments is a key part of the post-operative check and inspection of surgical instrument packaging. However, manual inventorying is prone to counting errors. The achievement of automated surgical instrument identification holds the potential to significantly mitigate the occurrence of medical accidents and reduce labor costs. In this paper, an improved You Only Look Once version 5 (YOLOv5) algorithm is proposed for the recognition of surgical instruments. Firstly, the squeeze-and-excitation (SE) attention module is added to the backbone to improve the feature extraction. Secondly, the loss function of YOLOv5 is improved with more global parameters to increase the convergence rate of the loss curve. Finally, an efficient convolution algorithm is added to the C3 module in the head to reduce computational complexity and memory usage. The experimental results show that our algorithm outperforms the original YOLOv5 with improvements observed across various metrics: mean average precision 50–95 (mAP50-95) achieved 88.7%, which improved by 1.8%, and computational requirements reduced by 39%. This study, with a simple but effective method, is expected to be a guide for automatically detecting, classifying, and sorting surgical instruments.

Multipitch estimation based on the iterative detection and separation of note events from single-channel polyphonic recordings.

Multiple fundamental frequency estimation has been extensively used in applications such as melody extraction, music transcription, instrument identification, and source separation. This paper presents an approach based on the iterative detection and extraction of note events, which are considered to be harmonic sounds characterised by a continuous pitch trajectory. Note events are assumed to be associated with musical notes being played by a single instrument, and their pitch trajectories are iteratively estimated. In every iteration, the pitch contour of the predominant note event is selected from a set of pitch estimates and used to separate its spectral energy from the input mixture in order to obtain a residual signal, which is then used as input in the next iteration. This iterative process stops when the energy of the residual is below a significance threshold. The pitch trajectories of all detected note events are then revised and reassembled to form the final set of pitch estimates for the original audio input. Evaluation of performance is conducted in different scenarios to show the potential of the proposed system, both in terms of its accuracy, and also as an initial stage in other complex tasks, such as note tracking and multipitch streaming.

Adjunctive Dental Procedures and Pain Assessment Among a Group of Patients Attending Dental University Hospital at King Saud University: A Cross-Sectional Study.

Background/purpose A large percentage of people still experience discomfort and pain during dental visits, even with advancements in instrumental techniques that enable dentists to manage patients in a pain-free manner. The aim of this study was to assess the prevalence andlevels of pain induced by various dental procedures, including adjunctive dental procedures. Material and methods A structured, custom-made questionnaire composed of 20 questions written in Arabic with accompanying pictures of instruments to simplify instrument identification for the patients was designed. The questionnaire was designed to investigate and determine the factors that cause pain and discomfort during dental procedures and was introduced to adult patients attending their dental appointments at the College of Dentistry. All data were collected using an online link that was distributed to patients attending dental clinics at the Dental University Hospital at King Saud University either through their mobile phones or the researcher's tablet. All data were entered and analyzed using SPSS version 25. Results A total of 158 patients participated in the study. The findings revealed that 50% of participants experienced pain from mouth mirror retraction and 28.48% experienced pain from suction. Participants also reported that the most common instruments associated with pain were the ultrasonic scaler (88.57%) and the periodontal probe (87.88%). Conclusion The findings provided valuable insights into the prevalence of pain during dental procedures and the factors that may contribute to this experience. Adjunctive dental procedures appeared to be causing a high prevalence of pain that could be avoided if dentists/dental assistants were more aware of it. These findings may have important implications for dental practitioners looking to reduce pain and improve patient experience during the provision of dental care.

Design of recognition algorithm for multiclass digital display instrument based on convolution neural network

Digital display instrument identification is a crucial approach for automating the collection of digital display data. In this study, we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments. We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory (LSTM). First, the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure. Next, the digital display instrument identification network was constructed based on a convolutional neural network using two-way LSTM and Connectionist temporal classification (CTC) of indefinite length. Finally, an automatic calibration system for digital display instruments was built, and a multiclass digital display instrument dataset was constructed by sampling in the system. We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.

Evolution of a surgical system using deep learning in minimally invasive surgery (Review).

Recently, artificial intelligence (AI) has been applied in various fields due to the development of new learning methods, such as deep learning, and the marked progress in computational processing speed. AI is also being applied in the medical field for medical image recognition and omics analysis of genomes and other data. Recently, AI applications for videos of minimally invasive surgeries have also advanced, and studies on such applications are increasing. In the present review, studies that focused on the following topics were selected: i) Organ and anatomy identification, ii) instrument identification, iii) procedure and surgical phase recognition, iv) surgery-time prediction, v) identification of an appropriate incision line, and vi) surgical education. The development of autonomous surgical robots is also progressing, with the Smart Tissue Autonomous Robot (STAR) and RAVEN systems being the most reported developments. STAR, in particular, is currently being used in laparoscopic imaging to recognize the surgical site from laparoscopic images and is in the process of establishing an automated suturing system, albeit in animal experiments. The present review examined the possibility of fully autonomous surgical robots in the future.

Impact of Financial Instrument Identification and Reclassification on Enterprises

Impact of Financial Instrument Identification and Reclassification on Enterprises

Current status of acute ischemic stroke management in Iran: Findings from a single-center study.

This study investigated the current status of acute ischemic stroke (AIS) management in an Iranian emergency department (ED). A descriptive study using a retrospective chart review was conducted on medical records of 270 patients with AIS who presented to the ED of a tertiary university hospital in the northeast of Iran from March 22 to September 22, 2019. The steps of this review process included instrument identification, medical records retrieval, data extraction, and data verification. Of patients with AIS, 88.9% (n = 240) did not receive stroke code activation. For the 11.1% of patients (n = 30) who received activation, 7% of codes (n = 19) were canceled by the acute stroke team and IV recombinant tissue plasminogen activator (r-tPA) was only administered for 4.1% of patients (n = 11). ED arrival outside 4.5 h from symptom onset was the main barrier to IV r-tPA administration for 83.8% of potentially eligible patients with AIS (n = 217). The median door-to-needle time was 70 min (interquartile range: 47-90 min). There was a better clinical performance in terms of critical time goals in potentially eligible patients with AIS if managed with stroke team activation compared to no stroke team activation.

Musical Instrument Identification Using Deep Learning Approach.

The work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata description and a review of related works. This is followed by showing the dataset prepared for the experiment and its division into subsets: training, validation, and evaluation. Then, the analyzed architecture of the neural network model is presented. Based on the described model, training is performed, and several quality metrics are determined for the training and validation sets. The results of the evaluation of the trained network on a separate set are shown. Detailed values for precision, recall, and the number of true and false positive and negative detections are presented. The model efficiency is high, with the metric values ranging from 0.86 for the guitar to 0.99 for drums. Finally, a discussion and a summary of the results obtained follows.

Analysis of Cataract Surgery Instrument Identification Performance of Convolutional and Recurrent Neural Network Ensembles Leveraging BigCat.

PurposeTo develop a method for accurate automated real-time identification of instruments in cataract surgery videos.MethodsCataract surgery videos were collected at University of Michigan's Kellogg Eye Center between 2020 and 2021. Videos were annotated for the presence of instruments to aid in the development, validation, and testing of machine learning (ML) models for multiclass, multilabel instrument identification.ResultsA new cataract surgery database, BigCat, was assembled, containing 190 videos with over 3.9 million annotated frames, the largest reported cataract surgery annotation database to date. Using a dense convolutional neural network (CNN) and a recursive averaging method, we were able to achieve a test F1 score of 0.9528 and test area under the receiver operator characteristic curve of 0.9985 for surgical instrument identification. These prove to be state-of-the-art results compared to previous works, while also only using a fraction of the model parameters of the previous architectures.ConclusionsAccurate automated surgical instrument identification is possible with lightweight CNNs and large datasets. Increasingly complex model architecture is not necessary to retain a well-performing model. Recurrent neural network architectures add additional complexity to a model and are unnecessary to attain state-of-the-art performance.Translational RelevanceInstrument identification in the operative field can be used for further applications such as evaluating surgical trainee skill level and developing early warning detection systems for use during surgery.

Combination of Resnet and Spatial Pyramid Pooling for Musical Instrument Identification

Abstract Identifying similar objects is one of the most challenging tasks in computer vision image recognition. The following musical instruments will be recognized in this study: French horn, harp, recorder, bassoon, cello, clarinet, erhu, guitar saxophone, trumpet, and violin. Numerous musical instruments are identical in size, form, and sound. Further, our works combine Resnet 50 with Spatial Pyramid Pooling (SPP) to identify musical instruments that are similar to one another. Next, the Resnet 50 and Resnet 50 SPP model evaluation performance includes the Floating-Point Operations (FLOPS), detection time, mAP, and IoU. Our work can increase the detection performance of musical instruments similar to one another. The method we propose, Resnet 50 SPP, shows the highest average accuracy of 84.64% compared to the results of previous studies.

Wearable Surgical Optical Tracking System Based on Multi-Modular Sensor Fusion

Optical tracking system (OTS) is an important part of surgical navigation and has been commonly employed in clinical practice due to high accuracy and precision. However, current OTS is bulky, which reduces the flexibility for clinical procedures. To address this drawback, we propose a wearable surgical instrument tracking system based on a multi-modular sensor fusion approach. The proposed tracking system consists of a multispectrum red–green–blue-depth (RGB-D) sensor and the accompanying tracking target. It significantly increases the agility of the tracking device while obtaining high tracking accuracy. We propose a plane-based refinement algorithm (PRA) to estimate marker centers, which ensures the accuracy and precision for the short baseline of the wearable stereo camera. A 2-D marker-based instrument identification algorithm (MIIA) has also been proposed to identify different instruments. Both simulations and real experiments have been carried out to validate the feasibility and performance of the proposed system. These accurate and robust experimental results have verified the feasibility of the proposed wearable surgical instrument tracking system.

An Automatic Instrument Recognition Approach Based on Deep Convolutional Neural Network

Background: This paper presents an automatic instrument recognition method highlighting the deep learning aspect of instrument identification in order to advance the automatic process of video monitoring remotely equipment of substation. Methods: This work utilizes the Scale Invariant Feature Transform approach (SIFT) and the Gaussian difference model for instrument positioning while proposing a design scheme of the instrument identification system. Results: The experimental outcomes obtained proves that the proposed system is capable of automatically recognizing a modest graphical interface and study independently while improving the effectiveness of the appliance, thereby, realizing the purpose of spontaneous self-check. The proposed approach is applicable for musical instrument recognition, and it provides 92% of the accuracy rate, 87.5% precision value and recall rate of 91.2%. Conclusion: The comparative analysis with other state-of-the-art methods justifies that the proposed deep learning-based music recognition method outperforms the other existing approaches in terms of accuracy, thereby providing a practicable music instrument recognition solution.

Instrument Identification Technology Based on Deep Learning

With the continuous improvement of science and technology, the substation remote control system has been constantly improved, which provides the possibility for the complete realization of intelligent and unmanned substation. However, due to the special substation environment, it is easy to cause interference, coupled with the low accuracy of today’s video image processing algorithm, which leads to the frequent occurrence of false alarms and missing alarms. Manual intervention is needed to deal with this, which inhibits the display of automatic intelligent substation processing functions. Therefore, in this paper, the most rapidly developed machine learning algorithm — deep learning is applied to the substation instrument equipment identification processing, in order to improve the accuracy and efficiency of instrument equipment identification, and make due contributions to the full realization of unattended substation.

Deep Neural Network for Musical Instrument Recognition Using MFCCs

The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain. Musical instrument recognition is the task of instrument identification by virtue of its audio. This audio, also termed as the sound vibrations are leveraged by the model to match with the instrument classes. In this paper, we use an artificial neural network (ANN) model that was trained to perform classification on twenty different classes of musical instruments. Here we use use only the mel-frequency cepstral coefficients (MFCCs) of the audio data. Our proposed model trains on the full London philharmonic orchestra dataset which contains twenty classes of instruments belonging to the four families viz. woodwinds, brass, percussion, and strings. Based on experimental results our model achieves state-of-the-art accuracy on the same.