Signal classification using Neural Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002

AUTHORS

Anna Esposito , Mariarosaria Falanga , Maria Funaro , Maria Marinaro , Silvia Scarpetta

ABSTRACT

The aim of this paper is to classify two kind of signals recorded by seismic station: artificial explosions and seismic activity. The problem is approached from both the preprocessing and the classification point of view. For the preprocessing stage, instead of the conventional Fourier Transform, we use a Linear Prediction Coding (LPC) algorithm, which allows to compress the data and extract robust features for the signal representation. For the classification stage, we have compared the performance of several neural models. An unsupervised method, based on the Principal Component Analysis (PCA) and the Mixture of Gaussian (MoG) clustering algorithm, gives a 70% percentage of correct classification. The Elman Recurrent Neural Nets (RNN) is able to reach 91% of correct classification on the test set. However this performance is strongly and critically dependent on the order of presentation of the events. Instead a MLP with a single hidden layer gives the 86% of correct classification on the test set, independently of the order of presentation of the patterns. More... »

PAGES

187-192

Book

TITLE

Neural Nets WIRN Vietri-01

ISBN

978-1-85233-505-2
978-1-4471-0219-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4471-0219-9_19

DOI

http://dx.doi.org/10.1007/978-1-4471-0219-9_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042120161


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Wright State University", 
          "id": "https://www.grid.ac/institutes/grid.268333.f", 
          "name": [
            "I.I.A.S.S., Vietri sul Mare, Salerno, IT", 
            "Dept.of Computer Science and Engineering, Wright State University, Dayton, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Esposito", 
        "givenName": "Anna", 
        "id": "sg:person.011031612133.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011031612133.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale Per La Fisica Della Materia", 
          "id": "https://www.grid.ac/institutes/grid.157869.4", 
          "name": [
            "Dipartimento di Scienze Fisiche \u2018E. R. Caianiello\u201d, Universita\u2019 di Salerno, SA, IT", 
            "INFM, Sezione di Salerno (SA), IT"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Falanga", 
        "givenName": "Mariarosaria", 
        "id": "sg:person.010777113161.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010777113161.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale Per La Fisica Della Materia", 
          "id": "https://www.grid.ac/institutes/grid.157869.4", 
          "name": [
            "Dipartimento di Scienze Fisiche \u2018E. R. Caianiello\u201d, Universita\u2019 di Salerno, SA, IT", 
            "INFM, Sezione di Salerno (SA), IT"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Funaro", 
        "givenName": "Maria", 
        "id": "sg:person.01224647200.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01224647200.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale Per La Fisica Della Materia", 
          "id": "https://www.grid.ac/institutes/grid.157869.4", 
          "name": [
            "Dipartimento di Scienze Fisiche \u2018E. R. Caianiello\u201d, Universita\u2019 di Salerno, SA, IT", 
            "INFM, Sezione di Salerno (SA), IT"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marinaro", 
        "givenName": "Maria", 
        "id": "sg:person.01027564003.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027564003.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale Per La Fisica Della Materia", 
          "id": "https://www.grid.ac/institutes/grid.157869.4", 
          "name": [
            "Dipartimento di Scienze Fisiche \u2018E. R. Caianiello\u201d, Universita\u2019 di Salerno, SA, IT", 
            "INFM, Sezione di Salerno (SA), IT"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Scarpetta", 
        "givenName": "Silvia", 
        "id": "sg:person.01134412304.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134412304.81"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1207/s15516709cog1402_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037432371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/proc.1975.9792", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061443031"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0129065789000475", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062899558"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2002", 
    "datePublishedReg": "2002-01-01", 
    "description": "The aim of this paper is to classify two kind of signals recorded by seismic station: artificial explosions and seismic activity. The problem is approached from both the preprocessing and the classification point of view. For the preprocessing stage, instead of the conventional Fourier Transform, we use a Linear Prediction Coding (LPC) algorithm, which allows to compress the data and extract robust features for the signal representation. For the classification stage, we have compared the performance of several neural models. An unsupervised method, based on the Principal Component Analysis (PCA) and the Mixture of Gaussian (MoG) clustering algorithm, gives a 70% percentage of correct classification. The Elman Recurrent Neural Nets (RNN) is able to reach 91% of correct classification on the test set. However this performance is strongly and critically dependent on the order of presentation of the events. Instead a MLP with a single hidden layer gives the 86% of correct classification on the test set, independently of the order of presentation of the patterns.", 
    "editor": [
      {
        "familyName": "Tagliaferri", 
        "givenName": "Roberto", 
        "type": "Person"
      }, 
      {
        "familyName": "Marinaro", 
        "givenName": "Maria", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4471-0219-9_19", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-85233-505-2", 
        "978-1-4471-0219-9"
      ], 
      "name": "Neural Nets WIRN Vietri-01", 
      "type": "Book"
    }, 
    "name": "Signal classification using Neural Networks", 
    "pagination": "187-192", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042120161"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4471-0219-9_19"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c8a36588b2100c64ecb58ba873dd38a7053eb5968a0bb196d53b56f76410be39"
        ]
      }
    ], 
    "publisher": {
      "location": "London", 
      "name": "Springer London", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4471-0219-9_19", 
      "https://app.dimensions.ai/details/publication/pub.1042120161"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T09:33", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000373_0000000373/records_13068_00000001.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-1-4471-0219-9_19"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-1-4471-0219-9_19'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4471-0219-9_19'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4471-0219-9_19'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4471-0219-9_19'


 

This table displays all metadata directly associated to this object as RDF triples.

112 TRIPLES      23 PREDICATES      30 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4471-0219-9_19 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Ne63f92373a844c8a81ddbf5130285751
4 schema:citation https://doi.org/10.1109/proc.1975.9792
5 https://doi.org/10.1142/s0129065789000475
6 https://doi.org/10.1207/s15516709cog1402_1
7 schema:datePublished 2002
8 schema:datePublishedReg 2002-01-01
9 schema:description The aim of this paper is to classify two kind of signals recorded by seismic station: artificial explosions and seismic activity. The problem is approached from both the preprocessing and the classification point of view. For the preprocessing stage, instead of the conventional Fourier Transform, we use a Linear Prediction Coding (LPC) algorithm, which allows to compress the data and extract robust features for the signal representation. For the classification stage, we have compared the performance of several neural models. An unsupervised method, based on the Principal Component Analysis (PCA) and the Mixture of Gaussian (MoG) clustering algorithm, gives a 70% percentage of correct classification. The Elman Recurrent Neural Nets (RNN) is able to reach 91% of correct classification on the test set. However this performance is strongly and critically dependent on the order of presentation of the events. Instead a MLP with a single hidden layer gives the 86% of correct classification on the test set, independently of the order of presentation of the patterns.
10 schema:editor Ncae3a3bf9d7c40a1903d8318d595da79
11 schema:genre chapter
12 schema:inLanguage en
13 schema:isAccessibleForFree false
14 schema:isPartOf Ne39df3a7c0ed42d2a1f3bb1b16484579
15 schema:name Signal classification using Neural Networks
16 schema:pagination 187-192
17 schema:productId N85889b00bb8040478f4efa90fc0b541b
18 Ne6e8fb18338b4ba69974ca5d7091096a
19 Nf122a85cd27a4f18ac8eeeeb6e609c9a
20 schema:publisher Neb9bcdabdb274449afb07acccec782b5
21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042120161
22 https://doi.org/10.1007/978-1-4471-0219-9_19
23 schema:sdDatePublished 2019-04-16T09:33
24 schema:sdLicense https://scigraph.springernature.com/explorer/license/
25 schema:sdPublisher N2813435be054412fb4070f19f0d6a371
26 schema:url https://link.springer.com/10.1007%2F978-1-4471-0219-9_19
27 sgo:license sg:explorer/license/
28 sgo:sdDataset chapters
29 rdf:type schema:Chapter
30 N00a47f5a565a40f5818d3b67e8327406 rdf:first sg:person.01134412304.81
31 rdf:rest rdf:nil
32 N2813435be054412fb4070f19f0d6a371 schema:name Springer Nature - SN SciGraph project
33 rdf:type schema:Organization
34 N449310c6b5e54db9814f26eaa07bf693 schema:familyName Marinaro
35 schema:givenName Maria
36 rdf:type schema:Person
37 N7f3fb4665b9e4a7e9766031edd628c1d rdf:first sg:person.010777113161.28
38 rdf:rest Naff0c90f6f904d7c99edc3327dc9015c
39 N85889b00bb8040478f4efa90fc0b541b schema:name dimensions_id
40 schema:value pub.1042120161
41 rdf:type schema:PropertyValue
42 N9ff40a7844a8499f99d7c72f81e60884 schema:familyName Tagliaferri
43 schema:givenName Roberto
44 rdf:type schema:Person
45 Na8089060cdaf47cd90f23bfbd1125f95 rdf:first N449310c6b5e54db9814f26eaa07bf693
46 rdf:rest rdf:nil
47 Naff0c90f6f904d7c99edc3327dc9015c rdf:first sg:person.01224647200.60
48 rdf:rest Neb2bc0a636e8470990f4b95bda628251
49 Ncae3a3bf9d7c40a1903d8318d595da79 rdf:first N9ff40a7844a8499f99d7c72f81e60884
50 rdf:rest Na8089060cdaf47cd90f23bfbd1125f95
51 Ne39df3a7c0ed42d2a1f3bb1b16484579 schema:isbn 978-1-4471-0219-9
52 978-1-85233-505-2
53 schema:name Neural Nets WIRN Vietri-01
54 rdf:type schema:Book
55 Ne63f92373a844c8a81ddbf5130285751 rdf:first sg:person.011031612133.55
56 rdf:rest N7f3fb4665b9e4a7e9766031edd628c1d
57 Ne6e8fb18338b4ba69974ca5d7091096a schema:name doi
58 schema:value 10.1007/978-1-4471-0219-9_19
59 rdf:type schema:PropertyValue
60 Neb2bc0a636e8470990f4b95bda628251 rdf:first sg:person.01027564003.17
61 rdf:rest N00a47f5a565a40f5818d3b67e8327406
62 Neb9bcdabdb274449afb07acccec782b5 schema:location London
63 schema:name Springer London
64 rdf:type schema:Organisation
65 Nf122a85cd27a4f18ac8eeeeb6e609c9a schema:name readcube_id
66 schema:value c8a36588b2100c64ecb58ba873dd38a7053eb5968a0bb196d53b56f76410be39
67 rdf:type schema:PropertyValue
68 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
69 schema:name Information and Computing Sciences
70 rdf:type schema:DefinedTerm
71 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
72 schema:name Artificial Intelligence and Image Processing
73 rdf:type schema:DefinedTerm
74 sg:person.01027564003.17 schema:affiliation https://www.grid.ac/institutes/grid.157869.4
75 schema:familyName Marinaro
76 schema:givenName Maria
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027564003.17
78 rdf:type schema:Person
79 sg:person.010777113161.28 schema:affiliation https://www.grid.ac/institutes/grid.157869.4
80 schema:familyName Falanga
81 schema:givenName Mariarosaria
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010777113161.28
83 rdf:type schema:Person
84 sg:person.011031612133.55 schema:affiliation https://www.grid.ac/institutes/grid.268333.f
85 schema:familyName Esposito
86 schema:givenName Anna
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011031612133.55
88 rdf:type schema:Person
89 sg:person.01134412304.81 schema:affiliation https://www.grid.ac/institutes/grid.157869.4
90 schema:familyName Scarpetta
91 schema:givenName Silvia
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134412304.81
93 rdf:type schema:Person
94 sg:person.01224647200.60 schema:affiliation https://www.grid.ac/institutes/grid.157869.4
95 schema:familyName Funaro
96 schema:givenName Maria
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01224647200.60
98 rdf:type schema:Person
99 https://doi.org/10.1109/proc.1975.9792 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061443031
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1142/s0129065789000475 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062899558
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1207/s15516709cog1402_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037432371
104 rdf:type schema:CreativeWork
105 https://www.grid.ac/institutes/grid.157869.4 schema:alternateName Istituto Nazionale Per La Fisica Della Materia
106 schema:name Dipartimento di Scienze Fisiche ‘E. R. Caianiello”, Universita’ di Salerno, SA, IT
107 INFM, Sezione di Salerno (SA), IT
108 rdf:type schema:Organization
109 https://www.grid.ac/institutes/grid.268333.f schema:alternateName Wright State University
110 schema:name Dept.of Computer Science and Engineering, Wright State University, Dayton, OH, USA
111 I.I.A.S.S., Vietri sul Mare, Salerno, IT
112 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...