scispace - formally typeset
Search or ask a question
Author

Srivatsan Sridhar

Bio: Srivatsan Sridhar is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Alice and Bob & Music information retrieval. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Papers
More filters
Proceedings ArticleDOI
01 Feb 2018
TL;DR: The use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold are shown to improve the detection of soft onsets in the vicinity of loud notes.
Abstract: Onset detection refers to the estimation of the timing of events in a music signal. It is an important sub-task in music information retrieval and forms the basis of high-level tasks such as beat tracking and tempo estimation. Typically, the onsets of new events in the audio such as melodic notes and percussive strikes are marked by short-time energy rises and changes in spectral distribution. However, each musical instrument is characterized by its own peculiarities and challenges. In this work, we consider the accurate detection of onsets in piano music. An annotated dataset is presented. The operations in a typical onset detection system are considered and modified based on specific observations on the piano music data. In particular, the use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold are shown to improve the detection of soft onsets in the vicinity of loud notes. We further present a grouping algorithm which reduces spurious onset detections.

3 citations

Book ChapterDOI
15 Dec 2019
TL;DR: This note shows that in fact, this message is also optimal in the protocol of Feige et al. (ISIT 2016), which improves on a previous result of Rajan et al., which showed this optimality restricted to protocols where Alice and Bob are deterministic.
Abstract: In an influential work aimed at understanding the communication requirements of secure computation, Feige, Kilian and Naor introduced a minimal model of secure computation (STOC 1994). In that work, among other results, Feige et al. presented a simple protocol for the 2 input AND function. It has remained an intriguing question whether the communication and randomness used in this protocol are optimal. While previous work of Data et al. (CRYPTO 2014) showed that the communication from the two parties with inputs (Alice and Bob) to the third party who gets the output is optimal, the question of optimality for the third message in the protocol – a common reference string shared between Alice and Bob – remained open. In this note we show that in fact, this message (and hence all the randomness used in the protocol) is also optimal in the protocol of Feige et al. This improves on a previous result of Rajan et al. (ISIT 2016), which showed this optimality restricted to protocols where Alice and Bob are deterministic. Further, our result holds even if only a weak secrecy condition is required of the protocol.

1 citations


Cited by
More filters
01 Jan 2018
TL;DR: FKN identified a set of simple requirements, showed that any function that satisfies these requirements is subject to the \(3k-O(1)\) lower-bound, and proved that a random function is likely to satisfy the requirements.
Abstract: Private simultaneous message (PSM) protocols were introduced by Feige, Kilian, and Naor (STOC ’94) as a minimal non-interactive model for information theoretic three-party secure computation. While it is known that every function $$f:\{0,1\}^k\times \{0,1\}^k \rightarrow \{0,1\}$$ admits a PSM protocol with exponential communication of $$2^{k/2}$$ (Beimel et al., TCC ’14), the best known (non-explicit) lower-bound is $$3k-O(1)$$ bits. To prove this lower-bound, FKN identified a set of simple requirements, showed that any function that satisfies these requirements is subject to the $$3k-O(1)$$ lower-bound, and proved that a random function is likely to satisfy the requirements. We revisit the FKN lower-bound and prove the following results: (Counterexample) We construct a function that satisfies the FKN requirements but has a PSM protocol with communication of $$2k+O(1)$$ bits, revealing a gap in the FKN proof. (PSM lower-bounds) We show that by imposing additional requirements, the FKN argument can be fixed leading to a $$3k-O(\log k)$$ lower-bound for a random function. We also get a similar lower-bound for a function that can be computed by a polynomial-size circuit (or even polynomial-time Turing machine under standard complexity-theoretic assumptions). This yields the first non-trivial lower-bound for an explicit Boolean function partially resolving an open problem of Data, Prabhakaran, and Prabhakaran (Crypto ’14, IEEE Information Theory ’16). We further extend these results to the setting of imperfect PSM protocols which may have small correctness or privacy error. (CDS lower-bounds) We show that the original FKN argument applies (as is) to some weak form of PSM protocols which are strongly related to the setting of Conditional Disclosure of Secrets (CDS). This connection yields a simple combinatorial criterion for establishing linear $$\varOmega (k)$$-bit CDS lower-bounds. As a corollary, we settle the complexity of the inner-product predicate resolving an open problem of Gay, Kerenidis, and Wee (Crypto ’15).

19 citations

Proceedings ArticleDOI
12 Oct 2020
TL;DR: This paper combines the advantages of conventional rule- based onset detection methods and convolutional neural network (CNN) based methods and proposes an advanced onset detection algorithm that has much better performance than both rule-based and existing CNN-based onset Detection methods.
Abstract: Onsets are criterion points to separate an audio signal into several notes. In this paper, we combine the advantages of conventional rule-based onset detection methods and convolutional neural network (CNN) based methods and propose an advanced onset detection algorithm. Different from rule-based methods, we apply the CNN to avoid tuning thresholds empirically. Different from existing CNN-based methods, which apply the original signal as the input directly, we construct a data with 204 feature layers and use it as the CNN input. Simulations show that the proposed algorithm has much better performance than both rule-based and existing CNN-based onset detection methods.

3 citations

Proceedings ArticleDOI
07 Jan 2022
TL;DR: In this article , the similarity of element-space to element and space-element to element has been used to improve the accuracy of melody matching, which is helpful for improving the performance of query-by-humming system.
Abstract: Dynamic programming (DP) is an effective algorithm to determine the similarity between two sequences. It plays an important role in text comparison, nucleotide sequence alignment, and melody matching. Conventional DP method performs element-to-element or element-to-space comparison and considers only the cases of replacement, deletion, and insertion. In this work, we improve the DP method by performing multiple element comparison. That is, in addition to perform element-wise comparison, we also compare the similarities of element-space to element and space-element to element. Moreover, the global similarity is also adopted to improve the accuracy of DP. Experiments show that, with the proposed algorithm, the accuracy of melody matching can be much improved. It is helpful for improving the performance of the query-by-humming system and applicable to other sequence comparison problems.
Proceedings ArticleDOI
29 Oct 2019
TL;DR: In this article, an interdisciplinary challenge is taken to combine the state of the art in both Interactive Architecture and Music Pattern Recognition fields into a prototype of interactive architecture called Doperah (A proposal for a Futuristic Dancing Opera House).
Abstract: The world is changing, the human being ever evolving individual, social and environmental needs cannot be addressed anymore by the traditional static buildings. Industry and research efforts are quickly moving toward the development of interactive architecture that dynamically responds to the changes in our environment, uses less energy, provides more occupant smartness, and features better overall space. In this paper, an interdisciplinary challenge is taken to combine the state of the art in both Interactive Architecture and Music Pattern Recognition fields into a prototype of interactive architecture called Doperah (A proposal for a Futuristic Dancing Opera House).