The Internet of Sounds: Convergent Trends, Insights, and Future Directions

Turchet, Luca; Lagrange, Mathieu; Rottondi, Cristina; Fazekas, György; Peters, Nils; Østergaard, Jan; Font, Frederic; Bäckström, Tom; Fischione, Carlo

doi:10.1109/jiot.2023.3253602

Cited by 32 publications

(5 citation statements)

References 237 publications

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“…In this way, the environmental cost of the Musical Thing is drastically reduced. Finally, concerning streaming, as it has a great environmental cost [17,36], we align with the recent recommendation suggested in the discourse on the Internet of Sounds [129]. Whenever it is possible, data messages rather than audio signals should be sent, and video should be streamed only when it is strictly necessary.…”

Section: ) First Strategy Related Examplesmentioning

confidence: 54%

“…The IoMusT is an emerging paradigm in computing that stems from the confluence of the fields of the IoT and music technology [124]. It belongs to the general field of the Internet of Sounds [125], which has recently received support by the IEEE with the creation of a dedicated Emerging Technology Initiative 2 . More specifically, the IoMusT refers to the networks of ''Musical Things'', which are smart objects serving a musical purpose.…”

Section: A the Internet Of Musical Thingsmentioning

confidence: 99%

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Sustainable Internet of Musical Things: Strategies to Account for Environmental and Social Sustainability in Network-Based Interactive Music Systems

Masu,

Merendino,

Rodã

et al. 2024

IEEE Access

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The use of internet-based and networking technology in computer music systems has greatly increased in the past few years. Such efforts fall in the remits of the emerging filed of the Internet of Musical Things (IoMusT), the extension of the Internet of Things paradigm to the musical domain. Given the increasing importance of connected devices in the musical domain, it is essential to reflect on the relationship between such systems and sustainability at the environmental and social levels. In this paper, we address this aspect from two perspectives: 1) how to design IoMusT systems in a sustainable way, and 2) how IoMusT systems can support sustainability. To this end, we relied on three lenses, combining literature from green IoT (lens 1), Sustainable HCI (lens 2), and the Sustainable Development Goals from the United Nations (lens 3). By combining these three lenses, we developed five strategies for a sustainable IoMusT, which are extensively presented and discussed providing critical reflections.

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Section: ) First Strategy Related Examplesmentioning

confidence: 54%

Section: A the Internet Of Musical Thingsmentioning

confidence: 99%

Sustainable Internet of Musical Things: Strategies to Account for Environmental and Social Sustainability in Network-Based Interactive Music Systems

Masu,

Merendino,

Rodã

et al. 2024

IEEE Access

Self Cite

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“…They rely on components which are equipped with sensors, actuators, communication and even processing capabilities, leading to the concept of ambient intelligence. Among all the available sensors, the acoustic modality is of particular interest, resulting in the emergence of the Internet of Sounds (IoS) [166]. A first advantage is that microphones are both inexpensive and easy to implement.…”

Section: Interaction With Connected Environmentsmentioning

confidence: 99%

“…They can also be shared in listening sessions to immerse people into these soundscapes, using the technologies which ensure the highest fidelity of recording, coding and reproduction. Acoustic monitoring is used in a growing number of contexts [166]: urban soundscapes to measure noise pollution or to detect dangerous situations (e.g. gun shots in cities), natural soundscapes for the ecoacoustics surveillance and protection of wildlife (e.g.…”

Section: Acoustic Monitoring Of Environmentsmentioning

confidence: 99%

Acoustic research for telecoms: bridging the heritage to the future

Nicol,

Monfort

2023

Acta Acust.

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In its early age, telecommunication was focused on voice communications, and acoustics was at the heart of the work related to speech coding and transmission, automatic speech recognition or speech synthesis, aiming at offering better quality (Quality of Experience or QoE) and enhanced services to users. As technology has evolved, the research themes have diversified, but acoustics remains essential. This paper gives an overview of the evolution of acoustic research for telecommunication. Communication was initially (and for a long time) only audio with a monophonic narrow-band sound (i.e. [300–3400 Hz]). After the bandwidth extension (from the wide-band [100–7000 Hz] to the full-band [20 Hz–20 kHz] range), a new break was the introduction of 3D sound, either to provide telepresence in audioconferencing or videoconferencing, or to enhance the QoE of contents such as radio, television, VOD, or video games. Loudspeaker or microphone arrays have been deployed to implement “Holophonic” or “Ambisonic” systems. The interaction between spatialized sounds and 3D images was also investigated. At the end of the 2000s, smartphones invaded our lives. Binaural sound was immediately acknowledged as the most suitable technology for reproducing 3D audio on smartphones. However, to achieve a satisfactory QoE, binaural filters need to be customized in relation with the listener’s morphology. This question is the main obstacle to a mass-market distribution of binaural sound, and its solving has prompted a large amount of work. In parallel with the development of technologies, their perceptual evaluation was an equally important area of research. In addition to conventional methods, innovative approaches have been explored for the assessment of sound spatialization, such as physiological measurement, neuroscience tools or Virtual Reality (VR). The latest development is the use of acoustics as a universal sensor for the Internet of Things (IoT) and connected environments. Microphones can be deployed, preferably with parcimony, in order to monitor surrounding sounds, with the goal of detecting information or events thanks to models of automatic sound recognition based on neural networks. Applications range from security and personal assistance to acoustic measurement of biodiversity. As for the control of environments or objects, voice commands have become widespread in recent years thanks to the tremendous progress made in speech recognition, but an even more intuitive mode based on direct control by the mind is proposed by Brain Computer Interfaces (BCIs), which rely on sensory stimulation using different modalities, among which the auditory one offers some advantages.

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“…Sound-based applications represent a revolutionary paradigm in the rapidly evolving landscape of Internet of Sound (IoS) technology, where audio signals serve as the primary medium for data transmission, control, and interaction among interconnected devices [1], [2]. Voice-activated module in an IoS system, such as smart home devices, voice banking, home automation systems, and virtual assistants, relies on recognizing the user's voice to activate critical functions and generally involve confidential information.…”

Section: Introductionmentioning

confidence: 99%

An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification

Pham

Ngo

Nguyen

et al. 2022

International Conference on Content-Based Multimedia Indexing

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In this paper, we propose a deep learning based system for the task of deepfake audio detection. In particular, the draw input audio is first transformed into various spectrograms using three transformation methods of Shorttime Fourier Transform (STFT), Constant-Q Transform (CQT), Wavelet Transform (WT) combined with different auditorybased filters of Mel, Gammatone, linear filters (LF), and discrete cosine transform (DCT). Given the spectrograms, we evaluate a wide range of classification models based on three deep learning approaches. The first approach is to train directly the spectrograms using our proposed baseline models of CNN-based model (CNN-baseline), RNN-based model (RNN-baseline), C-RNN model (C-RNN baseline). Meanwhile, the second approach is transfer learning from computer vision models such as ResNet-18, MobileNet-V3, EfficientNet-B0, DenseNet-121, SuffleNet-V2, Swint, Convnext-Tiny, GoogLeNet, MNASsnet, RegNet. In the third approach, we leverage the state-of-the-art audio pre-trained models of Whisper, Seamless, Speechbrain, and Pyannote to extract audio embeddings from the input spectrograms. Then, the audio embeddings are explored by a Multilayer perceptron (MLP) model to detect the fake or real audio samples. Finally, high-performance deep learning models from these approaches are fused to achieve the best performance. We evaluated our proposed models on ASVspoof 2019 benchmark dataset. Our best ensemble model achieved an Equal Error Rate (EER) of 0.03, which is highly competitive to top-performing systems in the ASVspoofing 2019 challenge.Experimental results also highlight the potential of selective spectrograms and deep learning approaches to enhance the task of audio deepfake detection.Items-deepfake audio, deep learning model, spectrogram, ASVspoof dataset.

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The Internet of Sounds: Convergent Trends, Insights, and Future Directions

Cited by 32 publications

References 237 publications

Sustainable Internet of Musical Things: Strategies to Account for Environmental and Social Sustainability in Network-Based Interactive Music Systems

Sustainable Internet of Musical Things: Strategies to Account for Environmental and Social Sustainability in Network-Based Interactive Music Systems

Acoustic research for telecoms: bridging the heritage to the future

An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification

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