Spatial Reverberation and Dereverberation Using an Acoustic Multiple-Input Multiple-Output System

Morgenstern, Hai; Rafaely, Boaz

doi:10.17743/jaes.2016.0063

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…However, some studies have shown that the results are not fully reproducible from one CDM to another [12] or from one center to another [13]. Some approaches argue that keeping the local conceptual model [14] and the local structural model [15] leads to better results. On the one hand, keeping MIMIC in its specific form will not solve the limitation for multicenter research, but on the other hand, a fully standardized form would introduce other disadvantages, such as loss of data and lower computational performances.…”

Section: Introductionmentioning

confidence: 99%

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study

Paris¹,

Lamer²,

Parrot³

2021

JMIR Med Inform

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Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.

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Section: Introductionmentioning

confidence: 99%

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study

Paris¹,

Lamer²,

Parrot³

2021

JMIR Med Inform

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show abstract

“…The git repository centralizes the various resources of this work such as documentation, source code, unit tests, as well as questioning examples, discussions and problem issues. It also indicates web resources such as the physical data model for MIMIC 11 and OMOP 12 datasets and the Achilles' web client 13 . All the code to create these statistics is provided on the article's framagit repository (see Repository Work section).…”

Section: Resultsmentioning

confidence: 99%

“…All the code to create these statistics is provided on the article's framagit repository (see Repository Work section). 9 http://blogs.aphp.fr/dat-icu/ 10 https://www.softwareheritage.org/ 11 https://mit-lcp.github.io/mimic-omop/schemaspymimic 12 https://mit-lcp.github.io/mimic-omop/schemaspyomop 13 https://mit-lcp.github.io/mimic-omop/AchillesWeb…”

Section: Resultsmentioning

confidence: 99%

“…However, some studies have shown that the results are not fully reproducible from one CDM to another [10] or from one centre to another [11]. Some approaches argue that keeping the local conceptual model [12] and the local structural model [13] leads to better results. On one hand, keeping MIMIC on its specific form will not solve the limitation for multicenter research but on the other hand, a fully standardized form would introduce other disadvantages, such as loss of data and lower computational performances.…”

Section: Introductionmentioning

confidence: 99%

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MIMIC in the OMOP Common Data Model

Paris

Parrot²

2020

Preprint

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Objectives : In the era of big data, the intensive care unit (ICU) is very likely to benefit from real-time computer analysis and modeling based on close patient mon- itoring and Electronic Health Record data. MIMIC is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools and experience to be shared. OMOP-CDM is spreading all over the world. The objective was to evaluate the difficulty to transform MIMIC into an OMOP (MIMIC-OMOP) database and the benefits of this transformation for analysts. Material & Method: A documented, tested, versioned, exemplified and open repository has been set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48- hour datathon. Result: With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables have been standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon with 160 participants and 15,000 requests executed with a maximum duration of one minute. Conclusion: The resulting MIMIC-OMOP dataset is the first MIMIC-OMOP dataset available free of charge with real disidentified data ready for replicable in- tensive care research. This approach can be generalized to any medical field.

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“…The listening experiment followed the protocol of the established MUSHRA test (MUltiple Stimuli with Hidden Reference and Anchor) [29], but without a hidden anchor. This approach seems useful when there is no standard anchor signal for the experiment [30]. Four different MUSHRA screens were generated for each of the acoustic environments described in Table I.…”

Section: A Methodologymentioning

confidence: 99%

The Effect of Partial Time-Frequency Masking of the Direct Sound on the Perception of Reverberant Speech

Madmoni

Tibor

Nelken

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

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The perception of sound in real-life acoustic environments, such as enclosed rooms or open spaces with reflective objects, is affected by reverberation. Hence, reverberation is extensively studied in the context of auditory perception, with many studies highlighting the importance of the direct sound for perception. Based on this insight, speech processing methods often use time-frequency (TF) analysis to detect TF bins that are dominated by the direct sound, and then use the detected bins to reproduce or enhance the speech signals. The detection of bins dominated by the direct sound is typically based on an objective measure, such as the direct-to-reverberant ratio (DRR). However, the relation between the DRR in the TF bins and the spatial perception of the reverberant sound which is reproduced from these bins is still not clear. It is the aim of this paper to provide some insights into this relation, specifically for reverberant speech, focusing on bins with high DRR. This is performed using a listening experiment, where high DRR bins within a reverberant speech signal have been masked in the TF domain, based on various DRR thresholds. The results show that the percentage of high-DRR TF bins that were masked may better indicate the quality of spatial perception, compared to the specific value of the DRR threshold. The insights from this work could be incorporated into spatial audio techniques that reproduce the direct sound of reverberant speech, and potentially improve spatial perception. This was illustrated with an implementation of directional audio coding that was studied with an additional listening experiment supporting the previously described results.

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Spatial Reverberation and Dereverberation Using an Acoustic Multiple-Input Multiple-Output System

Cited by 6 publications

References 38 publications

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study

MIMIC in the OMOP Common Data Model

The Effect of Partial Time-Frequency Masking of the Direct Sound on the Perception of Reverberant Speech

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