Soundscapes narrativos en el cine: tres bandas sonoras de Tōru Takemitsu (1962-1966)
Bibliographic record
Abstract
The present work proposes a model of music and sound analysis of film soundtracks by the Japanese composer Tōru Takemitsu in three collaborations with the director Hiroshi Teshigahara and the novelist, playwright and screenwriter Kōbō Abe: Pitfall (1962), Woman in the dunes (1964) and The face of another (1966). Takemitsu could be seeing in these productions as a precursor of the narrative soundscape composition applied to the field of cinema, since his musical and aesthetic thoughts on music composition for cinema had a great affinity with Canadian composer Raymond Murray Schafer’s concept of soundscape and his music philosophy. Mainly, due to their common interest in John Cage. The methodological approach combines acoustic environment categories of analysis provided by the field of soundscapes studies, other categories from the field of film music and sound studies, also categories that belongs to the traditional Japanese arts and music, and, in addition, categories developed by Takemitsu himself. We specially analyzed the narrative function of the soundtrack in relation to the different narrative levels proposed by the screenplay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".