SPECTRA: A SPEech proCessing plaTform as smaRtphone Application

Bisio, Igor; Lavagetto, Fabio; Marchese, Mario; Sciarrone, Andrea; Fra, Cristina; Valla, Massimo

doi:10.1109/icc.2015.7249447

Cited by 10 publications

(15 citation statements)

References 11 publications

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“…The algorithm uses GPS and HPS positioning information together with data received by WiFi to validate the users Check-Ins. Concerning the audio signal, [11] and [5] propose algorithms to recognize a speaker's characteristics, such as gender, identity, language, and emotional state, by using Support Vector Machines (SVMs) as classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we merge context-aware capabilities offered by smartphones with the TC paradigm. Specifically, we have employed a solution able to recognize the speaker's main characteristics, reported in [5], named SPECTRA. It uses a Support Vector Machine (SVM) classifier jointly with some specific audio features.…”

Section: Transient Context-aware Cloudmentioning

confidence: 99%

“…Here, the authors have employed an algorithm able to infer some of the main speaker's characteristics (e.g., gender, identity and language) from his voice signal (see [5] for all the technical details). For sake of brevity, this work only considers the gender recognition capability, but results and conclusions can be easily extended to the other cases of identity and language.…”

Section: Accuracy Of the Context-aware Algorithmmentioning

confidence: 99%

“…Obviously, considering that the TCAC only shares high-level information, employing the gender recognition algorithm on TCAC platform does not affect in any way the performance in terms of accuracy of the aforementioned algorithm with respect to its stand-alone usage. Table II reports the accuracy percentage of the gender recognition algorithm embedded within the TCAC when different audio features are employed, for the Cross Validation and the Open Set scenario, respectively (see [5] for details). From Table II it is worth noting that the gender recognition algorithm shows good performances (around 98% for the Open Set scenario) for all the employed features.…”

Section: Accuracy Of the Context-aware Algorithmmentioning

confidence: 99%

“…The transient cloud model allows for devices to rely on their collaborative efforts to provide computational services without access to the infrastructure [4]. To that end, this paper presents an architecture that fuses a context awareness algorithm (see [5]) with transient clouds (see [4]). Rather than focus on sharing physical resources such as the CPU, memory, storage, and network, we focus on sharing higher level functionalities that are driven by the context.…”

Section: Introductionmentioning

confidence: 99%

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Context Awareness over Transient Clouds

Sciarrone

Bisio

Lavagetto

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

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The exponential increase in the number and types of mobile devices, along with their ever-growing sets of capabilities, have enabled the development of new architectures that aim to harness such heterogeneity. Transient Clouds (TCs) are examples of mobile clouds which are created on-the-fly by the devices present in an environment to share their physical resources (e.g., CPU, memory, network) and would disappear as the nodes leave the network. They enable a device to go beyond its own physical limitations through utilizing the capabilities offered by nearby devices over an ad-hoc network. In this paper we present a Transient Context-Aware Cloud (TCAC) in which the nodes of the network care more about providing/learning higher level functionalities rather than lower level capabilities. We make the case for such an architecture in scenarios where it is not feasible for all the nodes to compute the context due to privacy, energy, and delay constraints rather than an unreachable network. We present a prototype implementation of our architecture over Android smartphones connected via WiFi along with the performance metrics (power/energy consumption and accuracy) to show the benefits of context awareness in TCs.

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Section: Related Workmentioning

confidence: 99%

Section: Transient Context-aware Cloudmentioning

confidence: 99%

Section: Accuracy Of the Context-aware Algorithmmentioning

confidence: 99%

Section: Accuracy Of the Context-aware Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Context Awareness over Transient Clouds

Sciarrone

Bisio

Lavagetto

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

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Context‐awareness over transient cloud in D2D networks: energy performance analysis and evaluation

Bisio

Lavagetto

Sciarrone

et al. 2015

Trans. Emerging Tel. Tech.

Self Cite

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The exponential increase in the number and types of mobile devices, along with their ever‐growing sets of capabilities, have enabled the development of new architectures that aim to harness such heterogeneity. Transient clouds are examples of mobile clouds, which are created on‐the‐fly by the devices present in an environment to share their physical resources (e.g. CPU, memory and network) and would disappear as the nodes leave the network. They enable a device to go beyond its own physical limitations through utilising the capabilities offered by nearby devices over an ad hoc network. This idea exploits the device‐to‐device (D2D) communications paradigm, which allows two nearby devices to communicate with each other in the licenced cellular bandwidth without a base station involved. In this paper, we present a transient context‐aware cloud (TCAC) paradigm based on the assumption that the nodes of the network care more about providing/learning higher level functionalities rather than lower level capabilities in D2D scenario. The proposed architecture, realised by using a WiFi Direct, can be portable through any paradigm, which exploits the D2D communications, so opening the doors to forthcoming 5G scenarios. We present a prototype implementation of our architecture over Android smartphones connected via WiFi Direct along with the performance metrics (power/energy consumption and accuracy) to show the benefits of TCAC. A theoretical and analytical model for the energy consumption related to a device within the TCAC is provided as well. Copyright © 2015 John Wiley & Sons, Ltd.

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GRaNN: feature selection with golden ratio-aided neural network for emotion, gender and speaker identification from voice signals

Garain¹,

Ray²,

Giampaolo

et al. 2022

Neural Comput & Applic

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Compared to other features of the human body, voice is quite complex and dynamic, in a sense that a speech can be spoken in various languages with different accents and in different emotional states. Recognizing the gender, i.e. male or female from the voice of an individual, is by all accounts a minor errand for human beings. Similar goes for speaker identification if we are well accustomed with the speaker for a long time. Our ears function as the front end, accepting the sound signs which our cerebrum processes and settles on our disposition. Although being trivial for us, it becomes a challenging task to mimic for any computing device. Automatic gender, emotion and speaker identification systems have many applications in surveillance, multimedia technology, robotics and social media. In this paper, we propose a Golden Ratio-aided Neural Network (GRaNN) architecture for the said purposes. As deciding the number of units for each layer in deep NN is a challenging issue, we have done this using the concept of Golden Ratio. Prior to that, an optimal subset of features are selected from the feature vector extracted, common for all three tasks, from spectral images obtained from the input voice signals. We have used a wrapper-filter framework where minimum redundancy maximum relevance selected features are fed to Mayfly algorithm combined with adaptive beta hill climbing (A$$\beta$$ β HC) algorithm. Our model achieves accuracies of 99.306% and 95.68% for gender identification in RAVDESS and Voice Gender datasets, 95.27% for emotion identification in RAVDESS dataset and 67.172% for speaker identification in RAVDESS dataset. Performance comparison of this model with existing models on the publicly available datasets confirms its superiority over those models. Results also ensure that we have chosen the common feature set meticulously, which works equally well on three different pattern classification tasks. The proposed wrapper-filter framework reduces the feature dimension significantly, thereby lessening the storage requirement and training time. Finally, strategically selecting the number units in each layer in NN help increases the overall performance of all three pattern classification tasks.

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SPECTRA: A SPEech proCessing plaTform as smaRtphone Application

Cited by 10 publications

References 11 publications

Context Awareness over Transient Clouds

Context Awareness over Transient Clouds

Context‐awareness over transient cloud in D2D networks: energy performance analysis and evaluation

GRaNN: feature selection with golden ratio-aided neural network for emotion, gender and speaker identification from voice signals

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