Efficient technique for ultra broadband, linear power amplifier design

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.In this paper, a 10 W ultra-broadband GaN power amplifier (PA) is designed, fabricated, and tested. The suggested design technique provides a more accurate starting point for matching network synthesis and better prediction of achievable circuit performance. A negative-image model was used to fit the extracted optimum impedances based on source-/load-pull technique and multi-section impedance matching networks were designed. The implemented amplifier presents an excellent broadband performance, resulting in a gain of 8.5 ± 0.5 dB, saturated output power of ≥10 W, and power added efficiency (PAE) of ≥23% over the whole bandwidth. The linearity performance has also been characterized. An output third-order intercept point (OIP3) of ≥45 dBm was extracted based on a two-tone measurement technique in the operating bandwidth with different frequency spacing values. The memory effect based on AM/AM and AM/PM conversions was also characterized using a modulated WiMAX signal of 10 MHz bandwidth at 5.8 GHz. Furthermore, a broadband Wilkinson combiner was designed for the same bandwidth with very low loss to extend the overall output power. Excellent agreement between simulated and measured PA performances was also achieved

Deep Polyphonic ADSR Piano Note Transcription

We investigate a late-fusion approach to piano transcription, combined with a strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM). The network architecture under consideration is compact in terms of its number of parameters and easy to train with gradient descent. The network outputs are fused over time in the final stage to obtain note segmentations, with an HMM whose transition probabilities are chosen based on a model of attack, decay, sustain, release (ADSR) envelopes, commonly used for sound synthesis. The note segments are then subject to a final binary decision rule to reject too weak note segment hypotheses. We obtain state-of-the-art results on the MAPS dataset, and are able to outperform other approaches by a large margin, when predicting complete note regions from onsets to offsets.Comment: 5 pages, 2 figures, published as ICASSP'1

Octave bandwidth S- and C-band GaN-HEMT power amplifiers for future 5G communication

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.In this contribution, a design methodology for octave-bandwidth power amplifiers (PA) for 5G communication systems using surface mount dual-flat-no-lead packaged gallium-nitride high-electron-mobility transistor devices is presented. Systematic source- and load-pull simulations have been used to find the optimum impedances across 75% fractional bandwidth for S- (1.9–4.2 GHz) and C-band (3.8–8.4 GHz) PAs. The harmonic impact is considered to improve the output power and efficiency of the PAs. Utilizing the characteristic behavior of the transistors leads to modified optimum fundamental load impedances for the low-frequency range, which have higher gain compared with high-frequency range, and minimize the influence of the higher harmonics. Continuous wave large-signal measurements of the realized S-Band PA show a power added efficiency (PAE) of more than 40% from 1.9–4.2 GHz and a flat power gain of 11 dB while achieving a saturated output power of 10 W. The measured performance of the C-Band PA demonstrates a delivered power between 3.5 and 5 W across the frequency range of 3.8–8.4 GHz. A flat power gain of around 9 ± 0.5 dB with 26–40% PAE is achieved

Addressing Tempo Estimation Octave Errors in Electronic Music by Incorporating Style Information Extracted From Wikipedia

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Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Evaluation of GaN-HEMT power amplifiers using BST-based components for load modulation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.In this paper, the concept of load-modulated power amplifiers (PAs) is studied. Two GaN-HEMT power amplifiers (PAs), targeted for high efficiency at maximum and output back-off (OBO) power levels, are designed, implemented, and tested across 1.8–2.2 GHz. The load modulation in the first design is realized by tuning the shunt capacitors in the output matching network. A novel method is employed in the second design, where barium–stronrium–titante is used for the realization of load modulation. The large-signal measurement results across the desired band show 59–70% drain efficiency at 44–44.5 dBm output power for both designs. Using the available tunable technique, the drain efficiency of the PAs is enhanced by 4–20% at 6 dB OBO across the bandwidth

A New Dataset for Amateur Vocal Percussion Analysis

The imitation of percussive instruments via the human voice is a natural way for us to communicate rhythmic ideas and, for this reason, it attracts the interest of music makers. Specifically, the automatic mapping of these vocal imitations to their emulated instruments would allow creators to realistically prototype rhythms in a faster way. The contribution of this study is two-fold. Firstly, a new Amateur Vocal Percussion (AVP) dataset is introduced to investigate how people with little or no experience in beatboxing approach the task of vocal percussion. The end-goal of this analysis is that of helping mapping algorithms to better generalise between subjects and achieve higher performances. The dataset comprises a total of 9780 utterances recorded by 28 participants with fully annotated onsets and labels (kick drum, snare drum, closed hi-hat and opened hi-hat). Lastly, we conducted baseline experiments on audio onset detection with the recorded dataset, comparing the performance of four state-of-the-art algorithms in a vocal percussion context

ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit

Dance and music are two highly correlated artistic forms. Synthesizing dance motions has attracted much attention recently. Most previous works conduct music-to-dance synthesis via directly music to human skeleton keypoints mapping. Meanwhile, human choreographers design dance motions from music in a two-stage manner: they firstly devise multiple choreographic dance units (CAUs), each with a series of dance motions, and then arrange the CAU sequence according to the rhythm, melody and emotion of the music. Inspired by these, we systematically study such two-stage choreography approach and construct a dataset to incorporate such choreography knowledge. Based on the constructed dataset, we design a two-stage music-to-dance synthesis framework ChoreoNet to imitate human choreography procedure. Our framework firstly devises a CAU prediction model to learn the mapping relationship between music and CAU sequences. Afterwards, we devise a spatial-temporal inpainting model to convert the CAU sequence into continuous dance motions. Experimental results demonstrate that the proposed ChoreoNet outperforms baseline methods (0.622 in terms of CAU BLEU score and 1.59 in terms of user study score).Comment: 10 pages, 5 figures, Accepted by ACM MM 202