216 research outputs found
A survey of sound source localization methods in wireless acoustic sensor networks
No full textWireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field
Zero-shot anomalous sound detection in domestic environments using large-scale pretrained audio pattern recognition models
No full textAnomalous sound detection is central to audio-based surveillance and monitoring. In a domestic environment, however, the classes of sounds to be considered anomalous are situation-dependent and cannot be determined in advance. At the same time, it is not feasible to expect a demanding labeling effort from the end user. To address these problems, we present a novel zero-shot method relying on an auxiliary large-scale pretrained audio neural network in support of an unsupervised anomaly detector. The auxiliary module is tasked to generate a fingerprint for each sound occasionally registered by the user. These fingerprints are then compared with those extracted from the input audio stream, and the resulting similarity score is used to increase or reduce the sensitivity of the base detector. Experimental results on synthetic data show that the proposed method substantially improves upon the unsupervised base detector and is capable of outperforming existing few-shot learning systems developed for machine condition monitoring without involving additional training
Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations
No full textAcoustic signal processing in the spherical harmonics domain (SHD) is an
active research area that exploits the signals acquired by higher order
microphone arrays. A very important task is that concerning the localization of
active sound sources. In this paper, we propose a simple yet effective method
to localize prominent acoustic sources in adverse acoustic scenarios. By using
a proper normalization and arrangement of the estimated spherical harmonic
coefficients, we exploit low-rank approximations to estimate the far field
modal directional pattern of the dominant source at each time-frame. The
experiments confirm the validity of the proposed approach, with superior
performance compared to other recent SHD-based approaches.Comment: To appear in ICASSP 202
Ectinogonia buqueti subsp. speciosa var. obscuripennis Cobos 1954
No full textEctinogonia buqueti speciosa var. obscuripennis Cobos, 1954:65 [Figures 31–33, 49–51] Non-type specimen: “El Sauce Coquimbo. Chile R. Wagenknecht c. // Coll. Olave Chile. Amer. m. // EX COLECCION Dr. A. Cobos // Cotipo // Ectinogonia buqueti ssp. speciosa var. obscuripennis nov. A. Cobos det. 1.953 // ¿Sintipo? MNCN Tipos N° 7943 // MNCN_Ent 275887” Non-type specimen: “Laguna Maule, 15-Enero.48, Cord: Tlaca,. Coll: L.E. Pena. // Ex Colección Dr. A. Cobos. // MNCN_Ent 275888”. Non-type specimen: “Las Cabras, Cord. Chillan, 1100, 1480 m., 10.23-Dic.54, Coll: L.E. Pena // Ex Colección Dr. A. Cobos. // MNCN_Ent 275889”. Non-type specimen: “Las Cabras, Cord. Chillan, 1100, 1480 m., 10.23-Dic.54, Coll: L.E. Pena // Ex Colección Dr. A. Cobos. // MNCN_Ent 275890”. Non-type specimen: “ V. Venado-Vilches, Cord. Talca. 1600, 29-XII-1973, Col. P. Vidal // Ex Colección Dr. A. Cobos. // Ectinogonia buqueti ssp. speciosa f. obscuripennis m. A. Cobos det. 1976// MNCN _Ent 275891”. [Male. Genital structure removed and glued on card]. Current status: This taxon was recently raised to species level by Anguita-Salinas et al. (2019), but the name and authorship should be updated as Ectinogonia (Ectinogonia) oscuripennis Moore, 1994. Moore’s aim was to give a new status to the variety obscuripennis Cobos, but he wrote Ectinogonia speciosa oscuripennis, so oscuripennis and not obscuripennis is the correct spelling for this taxon. Comments: As the name obscuripennis Cobos was used as an infrasubspecific one is therefore not available according to Article 45.5. (ICZN 1999) (a name expressly proposed to denote an infrasubspecific entity is not an available name). The name oscuripennis Moore, 1994 is the available name for obscuripennis Cobos, 1954 in agreement with Article 45.5.1. (When a subsequent author applies the same word to a species or subspecies in a manner that makes it an available name, even if he or she attributes authorship of the name to the author of its publication as an infrasubspecific name, that subsequent author thereby establishes a new name with its own authorship and date). Moore (1994:154) raised the name obscuripennis Cobos to the subspecies level as Ectinogonia speciosa oscuripennis. He does not mention the specimens studied by Cobos (1954:65) and makes the diagnosis studying seven specimens from "Las Trancas, Chillán, VIII Región, Dic. 1983, 16/I/1987 y Dic. 1988 ”, which are the syntypes of this taxon. Regarding the specimens identified by Cobos as f. obscuripennis, in the collection of the Institut Royal des Sciences Naturelles de Belgique (RBINS) is currently deposited the specimen mentioned by Cobos (1954:65) as a blackish specimen, which was previously determined by Charles Kerremans as Ectinogonia fastidiosa. This specimen bears the following labels: “ Ectinogonia buqueti ssp. speciosa var. obscuripennis nov. A Cobos det. 1.953 COTIPO Cf.: Rev. Chil. Ent. 1953,3:52–64,5 Para-type // Kerremans det., 1890: Ectinogonia fastidiosa Fairm. // Determ 1890 Kerremans // Coll. R. I.Sc.N.B. Coll. Weyers ”. In the MNCN-CSIC collection are preserved five specimens identified by Cobos as f. obscuripennis. One of them (Fig. 31–33) is labelled as Cotipo, but this specimen is not type of any available taxon. The specimen deposited at the RBINS collection (Fig. 49–51), and four of the five specimens deposited at the MNCN-CSIC collection (MNCN _Ent 275888, 275889, 275890 and 275891) are conspecific with the specimens studied by Moore (1994:154). The specimen MNCN _Ent 275887 (Fig. 31–33) is an unidentified species of Ectinogonia, which is not conspecific with this taxon.Published as part of Pineda, Cristian & París, Mercedes, 2022, Types of species of Ectinogonia (Coleoptera: Buprestidae) described by Antonio Cobos Sánchez deposited in the Museo Nacional de Ciencias Naturales, Madrid, pp. 478-486 in Zootaxa 5175 (4) on pages 480-481, DOI: 10.11646/zootaxa.5175.4.5, http://zenodo.org/record/700644
Deep-Learning-Based Radio Map Reconstruction for V2X Communications
No full textRadio environment map (REM) reconstruction based on large-scale channel measurements is a promising technology for future mobility services involving vehicle-to-everything (V2X) communications. REMs provide contextual information which can be exploited to reduce V2X communication latency and control signaling, for instance, through a fast access to channel state information. However, the accuracy of radio mapping techniques is limited by the availability of measurements, which require for collection significant signaling overhead. Moreover, mobility scenarios impose strict latency constraints that render fast channel acquisition a challenging problem. This paper presents a low-complexity deep-learning-based approach based on long-short term memory (LSTM) cells for REM reconstruction on roads, addressed as a data-filling problem. To improve model generalization, the network is trained on a virtually infinite dataset generated according to a 3GPP-compliant freeway scenario, considering different correlation properties and missing point configurations. The results show that the proposed approach provides a performance closer to the theoretical lower bound than the classical Ordinary Kriging spatial interpolation method, without increasing the complexity order. Experiments performed in realistic scenarios using a 3D city model confirm the generalization capability of the proposed solution
[es] GONZALO PÉREZ, FRANSCISCO DE COBOS Y EL LAZARILLO DE TORMES
No full textEl artículo aborda el problema de la autoría del Lazarillo de Tormes desde los estudios sobre la Corte, tomando en consideración la lectura en clave cortesana de la obra y la hipótesis presentada en su día por Dalai Brenes Carrillo. En ella se asociaba la figura de Lázaro de Tormes con la del secretario Gonzalo Pérez, y la del misterioso arcipreste de San Salvador con la de Francisco de los Cobos; asociación que, tras una profunda revisión histórica del caso, parece confirmarse, abriendo nuevos caminos al hispanismo para identificar, finalmente, al autor de la novela.PALABRAS CLAVE: Corte, cortesano, Pérez, Cobos, LazarilloGONZALO PÉREZ, FRANCISCO DE LOS COBOS AND THE LAZARILLO DE TORMESThe article approaches the problem of the authorship of the Lazarillo de Tormes from the Court studies, Taking in consideration the reading in court key of the work and the hypothesis presented by Dalai Brenes Carrillo. In wich, Lázaro de Tormes\u27s figure was associated with the secretary Gonzalo Perez, as well the figure of the mysterious archpriest of San Salvador was related with Francisco de los Cobos; association that, after a deep historical review of the case, seems to be confirmed, opening new ways for the hispanicism to identify, finally, the author of the novel.KEY WORDS: Court, courtier, Pérez, Cobos, Lazarill
Parametric head-related transfer function modeling and interpolation for cost-efficient binaural sound applications
No full textParametric methods for modeling the perceptually relevant features of head-related transfer functions (HRTFs) are very important for the development of low-cost immersive sound applications. This letter describes an efficient method based on a low-order infinite impulse response filter implemented by a chain of second order sections of conventional shelving and peak audio filters. The parameters (central frequency, gain, and quality factor) are numerically adjusted by iteratively fitting the frequency response of the filter to the desired HRTF. Besides allowing for low-order binaural models, the proposed approach provides an efficient way to synthesize HRTFs for non-measured angles by applying a simple interpolation between the parameters from neighboring responses. Additionally, the HRTF database size is significantly reduced. (C) 2013 Acoustical Society of America.The Spanish Ministry of Economy and Competitiveness and FEDER supported this work under the project TEC2012-37945-C02-01/02. Part of this work was also funded by Generalitat Valenciana Grant BEST2010.Ramos Peinado, G.; Cobos Serrano, M. (2013). Parametric head-related transfer function modeling and interpolation for cost-efficient binaural sound applications. Journal of the Acoustical Society of America. 134(3):1735-1738. https://doi.org/10.1121/1.4817881S17351738134
Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks
No full textThe interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized cross-correlations (GCCs) have been widely used for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a novel technique for TDE based on a sub-band analysis of the cross-power spectrum phase, providing a structured two-dimensional representation of the time delay information contained across different frequency bands. Inspired by deep-learning-based image denoising solutions, we propose in this paper the use of convolutional neural networks (CNNs) to learn the time-delay patterns contained in FS-GCCs extracted in adverse acoustic conditions. Our experiments confirm that the proposed approach provides excellent TDE performance while being able to generalize to different room and sensor setups
A note on the modified and mean-based steered-response power functionals for source localization in noisy and reverberant environments
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