Learning Parameter Spaces in Neural Modeling of Audio Circuits

This thesis studies black-box virtual analog modeling formulated as a machine learning sequence modeling task within the category of supervised learning problems. The focus is on learning scenarios where the modeling targets have multiple user controls, and the aim of the thesis is to evaluate how the properties of the training datasets affect the generalization of the learning algorithm. To study the problem, three nonlinear analogue sound processors were modeled using a recurrent neural network consisting of a Gated Recurrent Unit and a fully-connected output layer. For each target device, two groups of datasets, seven in total, were constructed, using SPICE simulations of the targets. The difference between the datasets is in the density of the sampling grid used for setting the user controls of the targets, as well as in the number of input/output pairs corresponding to each distinct value of each of the controls. For the targets considered during the study, the sparsest sampling grid using only three possible values for each of the user controls was found inadequate for the models to generalize over the testsets used for evaluation. Increasing the sampling density was seen improving the model performance in most cases, with some targets also portraying clear advantages with increasing the number of input/output pairs corresponding to each distinct value of the user controls. According to the study, a sampling grid with five points would appear as a good baseline for training neural networks on targets with multiple user controls when no further investigations in the sampling density can be afforded. For future work, the experiments could be extended to include global scaling of the dataset size while keeping the constraints for sampling the parameter spaces, as well as combining the data generation and training procedures to a single loop, allowing for potentially infinite variety within the datasets

Neural modeling of magnetic tape recorders

The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consists of three main components: the hysteretic nonlinearity and filtering jointly produced by the magnetic recording process as well as the record and playback amplifiers, the fluctuating delay originating from the tape transport, and the combined additive noise component from various electromagnetic origins. In our approach, the hysteretic nonlinear block is modeled using a recurrent neural network, while the delay trajectories and the noise component are generated using separate diffusion models, which employ U-net deep convolutional neural networks. According to the conducted objective evaluation, the proposed architecture faithfully captures the character of the magnetic tape recorder. The results of this study can be used to construct virtual replicas of vintage sound recording devices with applications in music production and audio antiquing tasks

Metabolic profiles reflect weight loss maintenance and the composition of diet after very-low-energy diet

Background & aims: Diet and weight loss affect circulating metabolome. However, metabolite profiles induced by different weight loss maintenance diets and underlying longer term weight loss maintenance remain unknown. Herein, we investigated after-weight-loss metabolic signatures of two isocaloric 24-wk weight maintenance diets differing in satiety value due to dietary fibre, protein and fat contents and identified metabolite features that associated with successful weight loss maintenance. Methods: Non-targeted LC-MS metabolomics approach was used to analyse plasma metabolites of 79 women and men (mean age \ub1 SD 49.7 \ub1 9.0 years; BMI 34.2 \ub1 2.5 kg/m2) participating in a weight management study. Participants underwent a 7-week very-low-energy diet (VLED) and were thereafter randomised into two groups for a 24-week weight maintenance phase. Higher satiety food (HSF) group consumed high-fibre, high-protein, and low-fat products, while lower satiety food (LSF) group consumed isocaloric low-fibre products with average protein and fat content as a part of their weight maintenance diets. Plasma metabolites were analysed before the VLED and before and after the weight maintenance phase. Metabolite features discriminating HSF and LSF groups were annotated. We also analysed metabolite features that discriminated participants who maintained ≥10% weight loss (HWM) and participants who maintained <10% weight loss (LWM) at the end of the study, irrespective of the diet. Finally, we assessed robust linear regression between metabolite features and anthropometric and food group variables. Results: We annotated 126 metabolites that discriminated the HSF and LSF groups and HWM and LWM groups (p < 0.05). Compared to LSF, the HSF group had lower levels of several amino acids, e.g. glutamine, arginine, and glycine, short-, medium- and long-chain acylcarnitines (CARs), odd- and even-chain lysoglycerophospholipids, and higher levels of fatty amides. Compared to LWM, the HWM group in general showed higher levels of glycerophospholipids with a saturated long-chain and a C20:4 fatty acid tail, and unsaturated free fatty acids (FFAs). Changes in several saturated odd- and even-chain LPCs and LPEs and fatty amides were associated with the intake of many food groups, particularly grain and dairy products. Increase in several (lyso)glycerophospholipids was associated with decrease in body weight and adiposity. Increased short- and medium-chain CARs were related to decreased body fat-free mass. Conclusions: Our results show that isocaloric weight maintenance diets differing in dietary fibre, protein, and fat content affected amino acid and lipid metabolism. Increased abundances of several phospholipid species and FFAs were related with greater weight loss maintenance. Our findings indicate common and distinct metabolites for weight and dietary related variables in the context of weight reduction and weight management. The study was registered in isrctn.org with identifier 67529475

Algoritminen syrjintä ja yhdenvertaisuuden edistäminen : Arviointikehikko syrjimättömälle tekoälylle

Tekoälyn kehitys on edennyt viime vuosina ennennäkemättömällä vauhdilla. Koneoppimiseen perustuvia algoritmeja pidetään lupaavina koulutuksen, terveydenhuollon, rekrytoinnin ja monen muun palvelun parantajana. Algoritminen päätöksenteko luo kuitenkin samaan aikaan myös uhkia yhdenvertaisuudelle ja syrjimättömyydelle. ”Tekoälyn vinoumien välttäminen: suomalainen arviointikehikko syrjimättömille tekoälysovelluksille” tutkimus- ja selvityshankkeen tavoitteena oli: Tuottaa uutta tietoa siitä, millaisia koneoppimiseen pohjautuvia tekoälyjärjestelmiä Suomessa on käytössä, millaiseen vaikutustenarviointiin ne pohjaavat ja mitä syrjiviä vaikutuksia niillä saattaa olla. Arvioida kriittisesti algoritmisten tekoälyjärjestelmien syrjiviä ja perusoikeudellisia vaikutuksia kartoituksen perusteella, huomioiden yhdenvertaisuuslain asettamat velvoitteet. Yhteiskehittää tutkimuksen perusteella arviointikehikko tekoälysovellusten syrjivien piirteiden tunnistamiseksi ja välttämiseksi sekä yhdenvertaisuuden edistämiseksi, ja luoda politiikkasuosituksia algoritmien sääntelyn kehittämiseksi. Tämä loppuraportti kokoaa yhteen hankkeen tulokset ja esittelee sen päätuotteena syntyneen arviointikehikon tekoälyjärjestelmien syrjinnän ja yhdenvertaisuuden arviointiin. Lisäksi tarjoamme politiikkasuosituksia arviointikehikon integrointiin osaksi valtionhallintoa ja vastuullista tekoälyn kehittämistä.Tämä julkaisu on toteutettu osana valtioneuvoston selvitys- ja tutkimussuunnitelman toimeenpanoa. (tietokayttoon.fi) Julkaisun sisällöstä vastaavat tiedon tuottajat, eikä tekstisisältö välttämättä edusta valtioneuvoston näkemystä

Sampling the user controls in neural modeling of audio devices

Abstract This work studies neural modeling of nonlinear parametric audio circuits, focusing on how the diversity of settings of the target device user controls seen during training affects network generalization. To study the problem, a large corpus of training datasets is synthetically generated using SPICE simulations of two distinct devices, an analog equalizer and an analog distortion pedal. A proven recurrent neural network architecture is trained using each dataset. The difference in the datasets is in the sampling resolution of the device user controls and in their overall size. Based on objective and subjective evaluation of the trained models, a sampling resolution of five for the device parameters is found to be sufficient to capture the behavior of the target systems for the types of devices considered during the study. This result is desirable, since a dense sampling grid can be impractical to realize in the general case when no automated way of setting the device parameters is available, while collecting large amounts of data using a sparse grid only incurs small additional costs. Thus, the result provides guidance for efficient collection of training data for neural modeling of other similar audio devices

Neural Modeling of Magnetic Tape Recorders

The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consists of three main components: the hysteretic nonlinearity and filtering jointly produced by the magnetic recording process as well as the record and playback amplifiers, the fluctuating delay originating from the tape transport, and the combined additive noise component from various electromagnetic origins. In our approach, the hysteretic nonlinear block is modeled using a recurrent neural network, while the delay trajectories and the noise component are generated using separate diffusion models, which employ U-net deep convolutional neural networks. According to the conducted objective evaluation, the proposed architecture faithfully captures the characterof the magnetic tape recorder. The results of this study can be used to construct virtual replicas of vintage sound recording devices with applications in music production and audio antiquing tasks.Peer reviewe

Changes in liver metabolic pathways demonstrate efficacy of the combined dietary and microbial therapeutic intervention in MASLD mouse model

Objective: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most prevalent liver disease globally, yet no therapies are approved. The effects of Escherichia coli Nissle 1917 expressing aldafermin, an engineered analog of the intestinal hormone FGF19, in combination with dietary change were investigated as a potential treatment for MASLD. Methods: MASLD was induced in C57BL/6J male mice by American lifestyle-induced obesity syndrome diet and then switched to a standard chow diet for seven weeks. In addition to the dietary change, the intervention group received genetically engineered E. coli Nissle expressing aldafermin, while control groups received either E. coli Nissle vehicle or no treatment. MASLD-related plasma biomarkers were measured using an automated clinical chemistry analyzer. The liver steatosis was assessed by histology and bioimaging analysis using Fiji (ImageJ) software. The effects of the intervention in the liver were also evaluated by RNA sequencing and liquid-chromatography-based non-targeted metabolomics analysis. Pathway enrichment studies were conducted by integrating the differentially expressed genes from the transcriptomics findings with the metabolites from the metabolomics results using Ingenuity pathway analysis. Results: After the intervention, E. coli Nissle expressing aldafermin along with dietary changes reduced body weight, liver steatosis, plasma aspartate aminotransferase, and plasma cholesterol levels compared to the two control groups. The integration of transcriptomics with non-targeted metabolomics analysis revealed the downregulation of amino acid metabolism and related receptor signaling pathways potentially implicated in the reduction of hepatic steatosis and insulin resistance. Moreover, the downregulation of pathways linked to lipid metabolism and changes in amino acid-related pathways suggested an overall reduction of oxidative stress in the liver. Conclusions: These data support the potential for using engineered microbial therapeutics in combination with dietary changes for managing MASLD

The recent northward expansion of Lymantria monacha in relation to realised changes in temperatures of different seasons

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