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Non-acoustic Speech Dataset

Non-acoustic speech sensing system based on flexible piezoelectric Version 1.0.0                                       This Read_Me.txt file briefly describes the non-acoustic speech dataset and instructions to access it.  The non-acoustic speech sensing system based on flexible piezoelectric is designed to satisfy specific needs around testing device models (in high-noise, complex environments). The system collected vibration signals from the jaws of six males and five females containing ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and is able to achieve 93.7% recognition accuracy by calculation. In general, this paper provides a non-acoustic speech dataset for Mandarin, including the parts collected, the number of people collected, and the environment. The dataset is available at: https://10.5281/zenodo.7090120 The data descriptor paper with details of data collection and cleaning process is under submission. For proper citation of the manuscript, please refer to the latest version of this dataset which includes the details. This dataset and its descriptor paper were created by: Shiji Yuan, Ying Sun, Dezhi Zheng, Xinlei Chen, Ying Ding,Shuai Wang, Shangchun Fan For questions or suggestions, please e-mail Dezhi Zheng <zhengdezhi@buaa.edu.cn> Description: Ten common words were chosen as the core of the vocabulary in this dataset. These ten command words can be used for commands in IoT or robotics applications: "forward", "backward", "right", "left", "stop", "up", "down", "draw", "drop", and "reset". The recording software is Adobe Audition2022,which adopts monophonic recording, 16-bit storage format, 16 kHz sampling frequency, and the recorded voice is saved in wav format. The dataset is provided with two storage rules, which are stored by subject number and corpus number as classification. In the first rule, the speech data of 11 subjects were stored in different folders with the subject serial number as the folder name. Each folder contains subfolders categorized by corpus. In the second rule, the speech data of ten corpus are stored in different folders, and the names of the folders are the corpus contents. The subject number, corpus number and record order are given for each data entry. For example, the data obtained when subject one recorded corpus 10 for the first time was labeled as 1-10_1. After the data collection process, a filtering algorithm for automatic detection of low non-acoustic speech data is designed to remove problematic data that are very short or very quiet. The script of the data filtering algorithm is provided in this repository.   For specific detail of the data filtering process, please refer to the script (speech data filtering algorithm in MATLAB) in this repository and the data descriptor paper. The dataset in this repository is the processed version. The raw dataset and removed audio files are not included in this repository. File list: Non-acoustic Speech Dataset.zip speech data filtering algorithm.zip Readme.txt

Results of Investigation of a Molecular Plasma From Its Acoustic Response

Result data set of the publication "Investigation of a Molecular Plasma From Its Acoustic Response"

Changes in the acoustic structure of Australian bird communities along a habitat complexity gradient

Avian vocalizations have evolved in response to a variety of abiotic and biotic selective pressures. While there is some support for signal convergence in similar habitats that is attributed to adaptation to the acoustic properties of the environment (the ‘acoustic adaptation hypothesis’, AAH), there is also evidence for character displacement as result of competition for signal space among coexisting species (the ‘acoustic niche partitioning hypothesis’). We explored the acoustic space of avian assemblages distributed along six different habitat types (from herbaceous habitats to warm rainforests) in south eastern Queensland, Australia. We employed three acoustic diversity indices (acoustic richness, evenness, and divergence) to characterize the signal space. In addition, we quantified the phylogenetic and morphological structure (in terms of both body mass and beak size) of each community. Acoustic parameters showed a moderately low phylogenetic signal, indicating labile evolution. Although, we did not find meaningful differences in acoustic diversity indices among habitat categories, there was a significant relationship between the regularity component (evenness) and vegetation height indicating that acoustic signals are more evenly distributed in dense habitats. After accounting for differences in species richness, the volume of acoustic space (i.e., acoustic richness) decreased as the level of phylogenetic and morphological resemblance among species in a given community increased. Additionally, we found a significantly negative relationship between acoustic divergence and divergence in body mass indicating that the less different species are in their body mass, the more different their songs are likely to be. This implies the existence of acoustic niche partitioning at community level. Overall, while we found mixed support for the AAH, our results suggest that community-level effects may play a role in structuring acoustic signals within avian communities in this region. This study shows that signal diversity estimated by diversity metrics of community ecology based on basic acoustic parameters can provide additional insight into the structure of animal vocalizations.

Non-acoustic speech sensing system based on flexible piezoelectric

The non-acoustic speech sensing system based on flexible piezoelectric is designed to satisfy specific needs around testing device models (in high-noise, complex environments). The system collected vibration signals from the jaws of six males and five females containing ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and is able to achieve 93.7% recognition accuracy by calculation. In general, this paper provides a non-acoustic speech dataset for Mandarin, including the parts collected, the number of people collected, and the environment. The dataset is available at: https://doi.org/10.5281/zenodo.7185663 The data descriptor paper with details of data collection and cleaning process is under submission. For proper citation of the manuscript, please refer to the latest version of this dataset which includes the details. This dataset and its descriptor paper were created by: Shiji Yuan, Ying Sun, Shuai Wang, Xinlei Chen,Ying Ding,Dezhi Zheng , Shangchun Fan For questions or suggestions, please e-mail Shuai Wang <wangshuai@buaa.edu.cn> Description: Ten common words were chosen as the core of the vocabulary in this dataset. These ten command words can be used for commands in IoT or robotics applications: "forward", "backward", "right", "left", "stop", "up", "down", "draw", "drop", and "reset". The recording was carried on by software named Adobe Audition2022. We set monophonic recording, 16-bit storage format, and 16 kHz sampling frequency before recording and saved the recorded voice in wav format.  The dataset is provided with two storage rules, which are stored by subject number and command number as classification. In the first rule, the speech data of 11 subjects were stored in different folders with the subject serial number as the folder name. Each folder contains subfolders categorized by command. In the second rule, the speech data of ten commands are stored in different folders, and the names of the folders are the command contents. The subject number, command number and record order are given for each data entry. For example, the data obtained when subject 1 recorded command 10 for the first time was labeled as "1-10_1". After the data collection process, a filtering algorithm for automatic detection of low non-acoustic speech data was designed to remove problematic data that were very short or very quiet.The script of the data filtering algorithm is provided in this repository.   For specific detail of the data filtering process, please refer to the script (speech data filtering algorithm in MATLAB) in this repository and the data descriptor paper. The dataset in this repository is the processed version. The raw dataset and removed audio files are not included in this repository. File list: Non-acoustic Speech Dataset.zip speech data filtering algorithm.zip Readme.txt

opencc-by-4.0Dec 2017View details →

Dataset for "Articulatory, Acoustic, and Prosodic Accommodation in a Cooperative Maze Task"

This dataset accompanies the article "Articulatory, Acoustic, and Prosodic Accommodation in a Cooperative Maze Task" in the journal PLoS ONE. A guide to the dataset, with information about how the data is organized, is also available within the repository.

opencc-by-4.0Oct 2020View details →

A model-based approach to acoustic reflector localization with a robotic platform

Constructing a spatial map of an indoor environment, e.g., a typical office environment with glass surfaces, is a difficult and challenging task. Current state-of-the-art, e.g., camera- and laser-based approaches are unsuitable for detecting transparent surfaces. Hence, the spatial map generated with these approaches are often inaccurate. In this paper, a method that utilizes echolocation with sound in the audible frequency range is proposed to robustly localize the position of an acoustic reflector, e.g., walls, glass surfaces etc., which could be used to construct a spatial map of an indoor environment as the robot moves. The proposed method estimate the acoustic reflector’s position, using only a single microphone and a loudspeaker that are present on many socially assistive robot platforms such as the NAO robot. The experimental results show that the proposed method could robustly detect an acoustic reflector up to a distance of 1.5 m in more than 60% of the trials and works efficiently even under low SNRs. To test the proposed method, a proof-of-concept robotic platform was build to construct a spatial map of an indoor environment. This dataset is made available with IROS 2020 paper: https://ieeexplore.ieee.org/abstract/document/9341437 code could be found on Github: https://github.com/irtiq7/iROS2020

opencc-by-4.0Mar 2022View details →

A framework for spatial map generation using acoustic echoes for robotic platforms

<div> <div> <div> <div> <div> <div> In this work, we present a framework for constructing a spatial map of an indoor environment using the concept of echolocation. More specifically, we propose a non-linear least squares (NLS) estimator which is combined with a spatial filtering technique, e.g., beamforming, to estimate both the time-of-arrival (TOA) and direction-of-arrival (DOA) of the acoustic echoes. The proposed framework is complemented with an echo detector to classify a spurious estimate and an acoustic reflector, i.e., a wall. Based on these estimators, we propose two algorithms that complement existing range sensors and aid robotic platforms in acoustic reflector localization and mapping: single-channel localization and mapping (ScLAM) and a multi-channel localization and mapping (McLAM). Compared to commonly used sensors, such as lidar, cameras and ultrasonic sensors, our proposed model-based approach can detect transparent surfaces that are typically found in an office environment and could work in audible frequency ranges. A proof-of-concept robotic platform was built to test our algorithms. According to our evaluation, both qualitative and quantitative experiments reveal that the proposed methods can detect an acoustic reflector up to a distance of 1.5 m at a signal-to-diffuse-noise ratio (SDNR) of 0 dB in a simulated environment and 10 dB in a real environment with an accuracy of 80%. </div> </div> </div> </div> </div> </div>

opencc-by-4.0May 2024View details →

Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring

The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification. If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows: Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2

opencc-by-4.0May 2024View details →

Acoustic Emission dataset for impact localization: numerical and experimental case studies

<h1>Acoustic Emission dataset for Defect Detection in Aluminum plates</h1> <h2>Simulated data</h2> <h3>File name: Simulation.zip</h3> Simulated AE signals based on a ray-tracing algorithm taking into consideration reflections with the mechanical boundaries of the medium (reflection up to the 4th order), which corresponds to a 1x1x0.003 m square aluminum plate. Each signal has been created by simulating the propagation between a transmitter (Tx, index from 1 to 40) and a Receiver (Rx, index from 1 to 25), grouped by Tx position and saved as a .mat struct ('data') containing the following fields: <ul> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Rx position</li> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Tx position</li> <li>data.data = 8000 x 25 matrix containing the transmitted, propagated AE signal from Tx to Rx organized by column. Each AE instance constitutes of 8000 samples acquired at a sampling frequency of 2 MHz (indicated in the file name), one for each Rx given that Tx position.</li> <li>data.Label = 1x25 vector containing the ToA labels associated with each of the 25 Tx-rx pairs (per Tx position) computed by means of the Akaike Information Criterion. </li> </ul> <h2>Experimental data</h2> <h3>File name: Test_x0.xx_y0.yyFs2MHz_1x1x0.003_Al.csv</h3> Experimental data collected with custom AE instrumentation as described in <a href="https://www.mdpi.com/1424-8220/22/3/1091">Ref 1.</a>  One single file is a collection of 3 tests (three repetitions of the impact event at the same position), each of them containing three signals acquired simultaneously by three sensors located in proximity of three corners of a 1x1x0.003 aluminum plate having the same geometrical and numerical characteristics of the numerical one. The sensors acquire 5000 samples at a rate of 2 MHz (indicated in the file name) with a pre-trigger window of 1500 samples. The specific coordinates of the sensors are: <ul> <li>s1 [x = 0.05, y = 0.95] m</li> <li>s2 [x = 0.05, y = 0.05] m</li> <li>s3 [x = 0.95, y = 0.05] m</li> </ul> There are 9 files associated with as many impact positions, indicated by the "x0.xx_y0.yy" entry in the file name, with 0.xx and 0.yy corresponding to the x and y coordinate, respectively. Excitation has been provided by means of a waveform generator exciting a 3-cycle sinusoidal wave with central frequency of 250 kHz. More deatils about the electronics and the full setup are provided in the same reference above.    This research work has been carried out within the Intelligent Sensor Systems Lab@University of Bologna, Italy.  For any needs, warning or curiosities, please contact federica.zonzini@unibo.it

opencc-by-4.0Apr 2024View details →

Fig. 2. A, male Nyctixalus margaritifer. B–C in A reference collection of the acoustic signals of male Nyctixalus margaritifer Boulenger, 1882 (Anura: Rhacophoridae)

Fig. 2. A, male Nyctixalus margaritifer. B–C, general structure of notes within a note group. B, one of the basic/fundamental note types. C, one of the longer notes.

opencc-by-4.0Apr 2024View details →

Fig. 1 in A reference collection of the acoustic signals of male Nyctixalus margaritifer Boulenger, 1882 (Anura: Rhacophoridae)

Fig. 1. Various note groups emitted by males of Nyctixalus margaritifer. A–B, note groups consisting of seven to eight notes from males from Telaga Warna, West Java. C) Note group of 12 notes emitted from a male from Mt. Slamet, Central Java.

opencc-by-4.0Mar 2010View details →

Figs 10–16 in A Characterisation Of The Pair Forming Acoustic Signals Of Isophya Harzi (Orthoptera, Tettigonioidea, Phaneropteridae)

Figs 10–16. Oscillograms of the male calling songs of three Isophya species producing acoustic signals with a similar basic structure: I. harzi (10, 13), I. beybienkoi (11, 14) and I. posthumoidalis (12, 15). And a two-traced oscillogram (16) of the male-female duet of I. harzi (ambient temperature 25.7 oC). Circumstances: 10, 13: Cozia Mountains, Romania, 26.1 oC; 11, 14: Slovak-karst, Slovakia, 27.6 oC; 12, 15: Maramures, Romania, 23.4 oC;

opencc-by-4.0Mar 2010View details →

Figs 1–8 in A Characterisation Of The Pair Forming Acoustic Signals Of Isophya Harzi (Orthoptera, Tettigonioidea, Phaneropteridae)

Figs 1–8. Oscillograms showing the amplitude modulation pattern of the calling songs of three males of Isophya harzi (male1: 1, 4, 7, 8; male2: 2, 5; male3: 3, 6) at three different time resolution (1–3; 4–6; 7–8). All recordings were made indoor at an ambient temperature of 26.1 °C.

opencc-by-4.0Mar 2010View details →

Fig. 9 in A Characterisation Of The Pair Forming Acoustic Signals Of Isophya Harzi (Orthoptera, Tettigonioidea, Phaneropteridae)

Fig. 9. Frequency spectrum of the male calling song of Isophya harzi (FFT size 2048, window-function Blackmann-Harris) based on a sound sample recorded using a Pettersson D240x ultrasound de-

opencc-by-4.0Dec 2023View details →

Figure 2 in The Distribution Of The Northern Bat Eptesicus Nilssonii (Keyserling & Blasius, 1839) In Latvia Assessed By Passive Acoustic Survey

Figure 2. Dot plot showing the activity of E. nilssonii in each observation site. The outer dot in each region represents the mean and the whiskers the standard error of the mean. Different letters indicate statistically significant differences (p<0.05) (A). Data visualization depicting the gradient of the activity of E. nilssonii in four parts of Latvia. The darker color represents the higher activity (B).

opencc-by-4.0Dec 2023View details →

Figure 1 in The Distribution Of The Northern Bat Eptesicus Nilssonii (Keyserling & Blasius, 1839) In Latvia Assessed By Passive Acoustic Survey

Figure 1. The map of Latvia divided in four regions under LKS92 25x25 km square network. Bat activity was studied in randomly selected squares (visited squares marked grey). In total, 60 squares were surveyed with six survey sites chosen in each square (n=360).

FIGURE 5 in INTRASPECIFIC VARIATION IN ACOUSTIC TRAITS AND BODY SIZE, AND NEW DISTRIBUTIONAL RECORDS FOR PSEUDOPALUDICOLA GIARETTAI CARVALHO, 2012 (ANURA, LEPTODACTYLIDAE, LEIUPERINAE): IMPLICATIONS FOR ITS CONGENERIC DIAGNOSIS

FIGURE 5: Scatterplot of the first two principal component scores (PCs) from acoustic traits of six populations of P. giarettai. Yellow (Curvelo; type locality); purple (Chapada Gaúcha); green (Coromandel); red (Buritis); blue (Buritizeiro); pink (Unaí).

FIGURE 3 in INTRASPECIFIC VARIATION IN ACOUSTIC TRAITS AND BODY SIZE, AND NEW DISTRIBUTIONAL RECORDS FOR PSEUDOPALUDICOLA GIARETTAI CARVALHO, 2012 (ANURA, LEPTODACTYLIDAE, LEIUPERINAE): IMPLICATIONS FOR ITS CONGENERIC DIAGNOSIS

FIGURE 3: Advertisement call of P. giarettai from the type locality (Curvelo, Minas Gerais). From top to bottom: oscillogram section (ca. 1.6 s) depicting calling pattern, waveform, spectrogram, and power spectrum of the second advertisement from oscillogram section. Sound energy with relative amplitude below 40 dB was clipped to zero dB to remove background noise from power spectrum.

FIGURE 2 in INTRASPECIFIC VARIATION IN ACOUSTIC TRAITS AND BODY SIZE, AND NEW DISTRIBUTIONAL RECORDS FOR PSEUDOPALUDICOLA GIARETTAI CARVALHO, 2012 (ANURA, LEPTODACTYLIDAE, LEIUPERINAE): IMPLICATIONS FOR ITS CONGENERIC DIAGNOSIS

FIGURE 2: Adult specimens of P. giarettai in life from: Above – Parque Nacional Grande Sertão Veredas, Chapada Gaúcha, Minas Gerais (voucher male AAG-UFU 1920: CRC = 14.6 mm); Below – Coromandel (female AAG-UFU 3566: CRC 18.8 mm).

FIGURE 4 in INTRASPECIFIC VARIATION IN ACOUSTIC TRAITS AND BODY SIZE, AND NEW DISTRIBUTIONAL RECORDS FOR PSEUDOPALUDICOLA GIARETTAI CARVALHO, 2012 (ANURA, LEPTODACTYLIDAE, LEIUPERINAE): IMPLICATIONS FOR ITS CONGENERIC DIAGNOSIS

FIGURE 4: Advertisement call of P. giarettai from the Parque Nacional Grande Sertão Veredas (Chapada Gaúcha, Minas Gerais). From top to bottom: oscillogram section (ca. 1.6 s) depicting calling pattern, waveform, spectrogram, and power spectrum of the second advertisement from oscillogram section. Sound energy with relative amplitude below 40 dB was clipped to zero dB to remove background noise from power spectrum.