IntelligentMusic Production

Man, Brecht De; Stables, Ryan; Reiss, Joshua D.

doi:10.4324/9781315166100

Cited by 5 publications

(7 citation statements)

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“…V menších prostrediach, kde veľká časť zvuku pochádza z javiska, má fyzická poloha bicích a gitarových zosilňovačov skutočný vplyv na zvuk a schopnosť publika priestorovo identifikovať rôzne nástroje. 104 Samotné nastavenia zvukových procesorov sú značne modulárne. Procesory môžu byť vložené v takmer neobmedzenom počte konfigurácií, a to najmä v digitálnom prostredí.…”

Section: Zvuková Mixážunclassified

“…statická mixáž (bez automatizovaných parametrov) je vytvorená skôr než automatizácie jej parametrov. 105 Mnohé príručky audiotechniky uvádzajú štandardné nastavenia pre mixovanie rôznych nástrojov a žánrov. Tie isté zdroje tiež uvádzajú, že zvuková mixáž je vysoko nelineárna a nepredvídateľná a že neexistujú žiadne prísne pravidlá, ktoré by sa mali dodržiavať.…”

Section: Zvuková Mixážunclassified

“…Založené sú na predpokladoch o vlastnostiach signálu a niekedy sú určené pre konkrétny nástroj. 106 Zvukoví majstri sú špecialisti, ktorí používajú dostupný komerčný softvér, často však majú vo svojich štúdiách aj špeciálne zvukové procesory. UI a strojové učenie je aj v profesionálnych štúdiách neoddeliteľnou súčasťou spracovania zvuku a často určuje spôsob, ako bude zvuková podoba populárnej nahrávky znieť.…”

Section: Zvuková Mixážunclassified

“…Tento proces zahŕňa zvukový strih a zvukové spracovanie stôp s dôrazom na umelecké vyznenie smerujúce k finálnej mixáži. 110 V preddigitálnej ére (v 50. až 80. rokoch 20. storočia) existoval lineárny, mechanický a analógový pracovný postup od strihu magnetickej pásky, miešania audio vrstiev zvukovým majstrom cez analógový mixpult až po proces konečnej verzie. V tomto období, aby sa predišlo poškodeniu zvukového záznamu, si proces výroby zvukového záznamu vyžadoval viac fyzickej zručnosti.…”

Section: Kultúra Remesla Zvukového Majstraunclassified

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Umelá inteligencia a hudba

Clement¹

2022

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Section: Zvuková Mixážunclassified

Section: Kultúra Remesla Zvukového Majstraunclassified

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Umelá inteligencia a hudba

Clement¹

2022

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“…The mixing engineer blends multiple tracks together by modifying acoustic properties such as dynamics and timbre [2]. A vast body of research has been exploring how this process can be automated through the use of intelligent tools [3][4][5][6]. Traditional Artificial Intelligence (AI) approaches such as expert systems have been adopted to create autonomous mixing tools [3].…”

Section: Introductionmentioning

confidence: 99%

Word Embeddings for Automatic Equalization in Audio Mixing

Venkatesh¹,

Moffat²,

Miranda³

2022

Preprint

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In recent years, machine learning has been widely adopted to automate the audio mixing process. Automatic mixing systems have been applied to various audio effects such as gain-adjustment, stereo panning, equalization, and reverberation. These systems can be controlled through visual interfaces, providing audio examples, using knobs, and semantic descriptors. Using semantic descriptors or textual information to control these systems is an effective way for artists to communicate their creative goals. Furthermore, sometimes artists use non-technical words that may not be understood by the mixing system, or even a mixing engineer. In this paper, we explore the novel idea of using word embeddings to represent semantic descriptors. Word embeddings are generally obtained by training neural networks on large corpora of written text. These embeddings serve as the input layer of the neural network to create a translation from words to EQ settings. Using this technique, the machine learning model can also generate EQ settings for semantic descriptors that it has not seen before. We perform experiments to demonstrate the feasibility of this idea. In addition, we compare the EQ settings of humans with the predictions of the neural network to evaluate the quality of predictions. The results showed that the embedding layer enables the neural network to understand semantic descriptors. We observed that the models with embedding layers perform better those without embedding layers, but not as good as human labels.

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To Catch A Chorus, Verse, Intro, or Anything Else: Analyzing a Song with Structural Functions

Wang¹,

Hung²,

Smith³

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., 'A', 'B', and 'C'). However, explicitly identifying the function of each segment (e.g., 'verse' or 'chorus') is rarely attempted, but has many applications. We introduce a multi-task deep learning framework to model these structural semantic labels directly from audio by estimating "verseness," "chorusness," and so forth, as a function of time. We propose a 7-class taxonomy (i.e., intro, verse, chorus, bridge, outro, instrumental, and silence) and provide rules to consolidate annotations from four disparate datasets. We also propose to use a spectral-temporal Transformer-based model, called SpecTNT, which can be trained with an additional connectionist temporal localization (CTL) loss. In cross-dataset evaluations using four public datasets, we demonstrate the effectiveness of the SpecTNT model and CTL loss, and obtain strong results overall: the proposed system outperforms state-of-the-art chorus-detection and boundary-detection methods at detecting choruses and boundaries, respectively.

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IntelligentMusic Production

Cited by 5 publications

References 0 publications

Umelá inteligencia a hudba

Umelá inteligencia a hudba

Word Embeddings for Automatic Equalization in Audio Mixing

To Catch A Chorus, Verse, Intro, or Anything Else: Analyzing a Song with Structural Functions

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