Paper year
2026
Paper dossier
Auditory Analysis of Tabla solo performances A study of Gharana specific characteristics
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
0
Authors
0
Topic labels
0
Source readout
Source and corpus status
Venue
Unknown venue
Source slug
unknown
Corpus placement
Controlled edge slice
Similarity rows
Not available yet
Ranking readout
Where this paper lands in the current run
Ranking details could not be loaded (API 503).
Abstract
Tabla is a pounding instrument in Hindustani classical music tradition. Tabla learning and presentation in the Indian landmass is based on stylistic schools called Gharana. Each Gharana is attributed by its main style of playing skills, act of Tabla beats, repertoire, compositions and improvisations. Recognizing the Gharana from a Tabla presentation is mainly helpful to set apparat the performance. The paper address the work of pre-programmed Gharana identified from solo Tabla recordings. I motivate the challenge and show various aspects and provocations in the task. I recognize an easy and diverse collection of over 16 hours of Tabla Sola recordings for the task. I suggest an approach using deep learning models that use an amalgam of convolutional neural networks(CNN) and long short term memory(LSTM) networks. The CNNs are used to draw out Gharana unfair features from the raw audio data. The LSTM networks are worth to classify the Gharana by using the sequence of draw out features from CNNs. Our demonstrations on Gharana recognition include different length of audio data and contrast between various aspect of the task. An expansion demonstrates a good result with highest recognition accuracy of 92% of Hindustani music as it keeps track of rhythm. It is not used only an accompaniment but also used in solo performances. Tabla solo is complex and elaborate, with a variety of pre-composed forms for uplifting further elaborations based on the player's stylistic schools called
Authors
No authors available.
Neighborhood labels
Topics
0 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Neighbor surface
Similar papers
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
No embedding-backed neighbors available for this paper/version yet.
Next handoff
Best next moves from here
01
Check recommendation families
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Inspect nearby topics
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Cross-check evaluation baselines
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.