Zenodo (CERN European Organization for Nuclear Research), Dec 4, 2022
FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of desi... more FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks.
International Journal of Rf and Microwave Computer-aided Engineering, Oct 18, 2019
In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed ... more In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed antenna is surrounded by metasurface-based unit cells. The unit cells are designed to reflect for the proposed frequency. Each of this unit cells are activated with the help of a diode. Unit cell is considered active by switching on the diode of respective unit cell. According to the activation of unit cell the pattern of the antenna will be reconfigured. The 2.45 GHz ISM band pattern reconfigurable microstrip antenna is presented. The radiation pattern of the antenna can be steered toward a desired direction by activating appropriate metasurface unit cell, minimizing the interference and optimizing medium usage. The proposed antenna performance is presented with the help of reflection coefficient and the pattern steerable capability by activating metasurface unit cells. The proposed antenna is having azimuth-pattern reconfigurable capability around 360 .
Proceedings of the 18th International Audio Mostly Conference
Tone Transfer is a novel deep-learning technique for interfacing a sound source with a synthesize... more Tone Transfer is a novel deep-learning technique for interfacing a sound source with a synthesizer, transforming the timbre of audio excerpts while keeping their musical form content. Due to its good audio quality results and continuous controllability, it has been recently applied in several audio processing tools. Nevertheless, it still presents several shortcomings related to poor sound diversity, and limited transient and dynamic rendering, which we believe hinder its possibilities of articulation and phrasing in a real-time performance context. In this work, we present a discussion on current Tone Transfer architectures for the task of controlling synthetic audio with musical instruments and discuss their challenges in allowing expressive performances. Next, we introduce Envelope Learning, a novel method for designing Tone Transfer architectures that map musical events using a training objective at the synthesis parameter level. Our technique can render note beginnings and endings accurately and for a variety of sounds; these are essential steps for improving musical articulation, phrasing, and sound diversity with Tone Transfer. Finally, we implement a VST plugin for real-time live use and discuss possibilities for improvement. CCS CONCEPTS • Applied computing → Sound and music computing; • Computing methodologies → Neural networks; • Human-centered computing → Interaction techniques.
FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of desi... more FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks.
Cutting edge embedded systems have always been a part of NIME's practices. Lowresource computing ... more Cutting edge embedded systems have always been a part of NIME's practices. Lowresource computing hardware, such as microcontrollers or single-board computers, can be embedded into digital musical instruments or interfaces to perform specific functions such as real-time digital signal processing of sensor data and sound [1][2][3] [4][5]. Simultaneously, an interest in exploiting the creative potentials of artificial intelligence (AI) for instrument design and musical expression has been growing within the NIME community in the past years [6][7][8][9][10][11]. Recent advancements in embedded computing have allowed for faster and more intensive computation capabilities [12]. However, the deployment of machine learning or symbolic AI techniques still presents several technical challenges (e.g., data bandwidth, memory handling) and higher-level design constraints [13][14][15][16]. Some of these challenges are general to embedded systems, while others are specific
International Journal of RF and Microwave Computer-Aided Engineering, 2019
In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed ... more In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed antenna is surrounded by metasurface-based unit cells. The unit cells are designed to reflect for the proposed frequency. Each of this unit cells are activated with the help of a diode. Unit cell is considered active by switching on the diode of respective unit cell. According to the activation of unit cell the pattern of the antenna will be reconfigured. The 2.45 GHz ISM band pattern reconfigurable microstrip antenna is presented. The radiation pattern of the antenna can be steered toward a desired direction by activating appropriate metasurface unit cell, minimizing the interference and optimizing medium usage. The proposed antenna performance is presented with the help of reflection coefficient and the pattern steerable capability by activating metasurface unit cells. The proposed antenna is having azimuth-pattern reconfigurable capability around 360 .
Zenodo (CERN European Organization for Nuclear Research), Dec 4, 2022
FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of desi... more FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks.
International Journal of Rf and Microwave Computer-aided Engineering, Oct 18, 2019
In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed ... more In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed antenna is surrounded by metasurface-based unit cells. The unit cells are designed to reflect for the proposed frequency. Each of this unit cells are activated with the help of a diode. Unit cell is considered active by switching on the diode of respective unit cell. According to the activation of unit cell the pattern of the antenna will be reconfigured. The 2.45 GHz ISM band pattern reconfigurable microstrip antenna is presented. The radiation pattern of the antenna can be steered toward a desired direction by activating appropriate metasurface unit cell, minimizing the interference and optimizing medium usage. The proposed antenna performance is presented with the help of reflection coefficient and the pattern steerable capability by activating metasurface unit cells. The proposed antenna is having azimuth-pattern reconfigurable capability around 360 .
Proceedings of the 18th International Audio Mostly Conference
Tone Transfer is a novel deep-learning technique for interfacing a sound source with a synthesize... more Tone Transfer is a novel deep-learning technique for interfacing a sound source with a synthesizer, transforming the timbre of audio excerpts while keeping their musical form content. Due to its good audio quality results and continuous controllability, it has been recently applied in several audio processing tools. Nevertheless, it still presents several shortcomings related to poor sound diversity, and limited transient and dynamic rendering, which we believe hinder its possibilities of articulation and phrasing in a real-time performance context. In this work, we present a discussion on current Tone Transfer architectures for the task of controlling synthetic audio with musical instruments and discuss their challenges in allowing expressive performances. Next, we introduce Envelope Learning, a novel method for designing Tone Transfer architectures that map musical events using a training objective at the synthesis parameter level. Our technique can render note beginnings and endings accurately and for a variety of sounds; these are essential steps for improving musical articulation, phrasing, and sound diversity with Tone Transfer. Finally, we implement a VST plugin for real-time live use and discuss possibilities for improvement. CCS CONCEPTS • Applied computing → Sound and music computing; • Computing methodologies → Neural networks; • Human-centered computing → Interaction techniques.
FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of desi... more FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks.
Cutting edge embedded systems have always been a part of NIME's practices. Lowresource computing ... more Cutting edge embedded systems have always been a part of NIME's practices. Lowresource computing hardware, such as microcontrollers or single-board computers, can be embedded into digital musical instruments or interfaces to perform specific functions such as real-time digital signal processing of sensor data and sound [1][2][3] [4][5]. Simultaneously, an interest in exploiting the creative potentials of artificial intelligence (AI) for instrument design and musical expression has been growing within the NIME community in the past years [6][7][8][9][10][11]. Recent advancements in embedded computing have allowed for faster and more intensive computation capabilities [12]. However, the deployment of machine learning or symbolic AI techniques still presents several technical challenges (e.g., data bandwidth, memory handling) and higher-level design constraints [13][14][15][16]. Some of these challenges are general to embedded systems, while others are specific
International Journal of RF and Microwave Computer-Aided Engineering, 2019
In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed ... more In this article, a Z-shaped antenna is designed for 2.45 GHz ISM band applications. The proposed antenna is surrounded by metasurface-based unit cells. The unit cells are designed to reflect for the proposed frequency. Each of this unit cells are activated with the help of a diode. Unit cell is considered active by switching on the diode of respective unit cell. According to the activation of unit cell the pattern of the antenna will be reconfigured. The 2.45 GHz ISM band pattern reconfigurable microstrip antenna is presented. The radiation pattern of the antenna can be steered toward a desired direction by activating appropriate metasurface unit cell, minimizing the interference and optimizing medium usage. The proposed antenna performance is presented with the help of reflection coefficient and the pattern steerable capability by activating metasurface unit cells. The proposed antenna is having azimuth-pattern reconfigurable capability around 360 .
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Papers by Franco Caspe