Research article Special Issues

Inter classifier comparison to detect voice pathologies

  • Received: 19 December 2020 Accepted: 26 February 2021 Published: 05 March 2021
  • Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfunctioning. In medical science, novel machine learning algorithms are applied to construct a system to identify disorders that occur invoice. This study aims to extract the features from the audio signals of four chosen diseases from the SVD dataset, such as laryngitis, cyst, non-fluency syndrome, and dysphonia, and then compare the four results of machine learning algorithms, i.e., SVM, Naïve Byes, decision tree and ensemble classifier. In this project, we have used a comparative approach along with the new combination of features to detect voice pathologies which are laryngitis, cyst, non-fluency syndrome, and dysphonia from the SVD dataset. The combination of specific 13 MFCC (mel-frequency cepstral coefficients) features along with pitch, zero crossing rate (ZCR), spectral flux, spectral entropy, spectral centroid, spectral roll-off, and short term energy for more accurate detection of voice pathologies. It is proven that the combination of features extracted gives the best product on the audio, which split into 10 ms. Four machine learning classifiers, SVM, Naïve Bayes, decision tree and ensemble classifier for the inter classifier comparison, give 93.18, 99.45,100 and 51%, respectively. Out of these accuracies, both Naïve Bayes and the decision tree show the most promising results with a higher detection rate. Naïve Bayes and decision tree gives the highest reported outcomes on the selected set of features in the proposed methodology. The SVM has also been concluded to be the commonly used voice condition identification algorithm.

    Citation: Sidra Abid Syed, Munaf Rashid, Samreen Hussain, Anoshia Imtiaz, Hamnah Abid, Hira Zahid. Inter classifier comparison to detect voice pathologies[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2258-2273. doi: 10.3934/mbe.2021114

    Related Papers:

    [1] Hanpeng Gao, Yunlong Zhou, Yuanfeng Zhang . Sincere wide τ-tilting modules. Electronic Research Archive, 2025, 33(4): 2275-2284. doi: 10.3934/era.2025099
    [2] Rongmin Zhu, Tiwei Zhao . The construction of tilting cotorsion pairs for hereditary abelian categories. Electronic Research Archive, 2025, 33(5): 2719-2735. doi: 10.3934/era.2025120
    [3] Yajun Ma, Haiyu Liu, Yuxian Geng . A new method to construct model structures from left Frobenius pairs in extriangulated categories. Electronic Research Archive, 2022, 30(8): 2774-2787. doi: 10.3934/era.2022142
    [4] Haiyu Liu, Rongmin Zhu, Yuxian Geng . Gorenstein global dimensions relative to balanced pairs. Electronic Research Archive, 2020, 28(4): 1563-1571. doi: 10.3934/era.2020082
    [5] Dongxing Fu, Xiaowei Xu, Zhibing Zhao . Generalized tilting modules and Frobenius extensions. Electronic Research Archive, 2022, 30(9): 3337-3350. doi: 10.3934/era.2022169
    [6] Agustín Moreno Cañadas, Robinson-Julian Serna, Isaías David Marín Gaviria . Zavadskij modules over cluster-tilted algebras of type A. Electronic Research Archive, 2022, 30(9): 3435-3451. doi: 10.3934/era.2022175
    [7] Shengxiang Wang, Xiaohui Zhang, Shuangjian Guo . The Hom-Long dimodule category and nonlinear equations. Electronic Research Archive, 2022, 30(1): 362-381. doi: 10.3934/era.2022019
    [8] Jiangsheng Hu, Dongdong Zhang, Tiwei Zhao, Panyue Zhou . Balance of complete cohomology in extriangulated categories. Electronic Research Archive, 2021, 29(5): 3341-3359. doi: 10.3934/era.2021042
    [9] Xiu-Jian Wang, Jia-Bao Liu . Quasi-tilted property of generalized lower triangular matrix algebras. Electronic Research Archive, 2025, 33(5): 3065-3073. doi: 10.3934/era.2025134
    [10] János Kollár . Relative mmp without Q-factoriality. Electronic Research Archive, 2021, 29(5): 3193-3203. doi: 10.3934/era.2021033
  • Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfunctioning. In medical science, novel machine learning algorithms are applied to construct a system to identify disorders that occur invoice. This study aims to extract the features from the audio signals of four chosen diseases from the SVD dataset, such as laryngitis, cyst, non-fluency syndrome, and dysphonia, and then compare the four results of machine learning algorithms, i.e., SVM, Naïve Byes, decision tree and ensemble classifier. In this project, we have used a comparative approach along with the new combination of features to detect voice pathologies which are laryngitis, cyst, non-fluency syndrome, and dysphonia from the SVD dataset. The combination of specific 13 MFCC (mel-frequency cepstral coefficients) features along with pitch, zero crossing rate (ZCR), spectral flux, spectral entropy, spectral centroid, spectral roll-off, and short term energy for more accurate detection of voice pathologies. It is proven that the combination of features extracted gives the best product on the audio, which split into 10 ms. Four machine learning classifiers, SVM, Naïve Bayes, decision tree and ensemble classifier for the inter classifier comparison, give 93.18, 99.45,100 and 51%, respectively. Out of these accuracies, both Naïve Bayes and the decision tree show the most promising results with a higher detection rate. Naïve Bayes and decision tree gives the highest reported outcomes on the selected set of features in the proposed methodology. The SVM has also been concluded to be the commonly used voice condition identification algorithm.



    In [1] (see [2] for type A), the authors introduced cluster categories which were associated to finite dimensional hereditary algebras. It is well known that cluster-tilting theory gives a way to construct abelian categories from some triangulated and exact categories.

    Recently, Nakaoka and Palu introduced extriangulated categories in [3], which are a simultaneous generalization of exact categories and triangulated categories, see also [4,5,6]. Subcategories of an extriangulated category which are closed under extension are also extriangulated categories. However, there exist some other examples of extriangulated categories which are neither exact nor triangulated, see [6,7,8].

    When T is a cluster tilting subcategory, the authors Yang, Zhou and Zhu [9, Definition 3.1] introduced the notions of T[1]-cluster tilting subcategories (also called ghost cluster tilting subcategories) and weak T[1]-cluster tilting subcategories in a triangulated category C, which are generalizations of cluster tilting subcategories. In these works, the authors investigated the relationship between C and modT via the restricted Yoneda functor G more closely. More precisely, they gave a bijection between the class of T[1]-cluster tilting subcategories of C and the class of support τ-tilting pairs of modT, see [9, Theorems 4.3 and 4.4].

    Inspired by Yang, Zhou and Zhu [9] and Liu and Zhou [10], we introduce the notion of relative cluster tilting subcategories in an extriangulated category B. More importantly, we want to investigate the relationship between relative cluster tilting subcategories and some important subcategories of modΩ(T)_ (see Theorem 3.9 and Corollary 3.10), which generalizes and improves the work by Yang, Zhou and Zhu [9] and Liu and Zhou [10].

    It is worth noting that the proof idea of our main results in this manuscript is similar to that in [9, Theorems 4.3 and 4.4], however, the generalization is nontrivial and we give a new proof technique.

    Throughout the paper, let B denote an additive category. The subcategories considered are full additive subcategories which are closed under isomorphisms. Let [X](A,B) denote the subgroup of HomB(A,B) consisting of morphisms which factor through objects in a subcategory X. The quotient category B/[X] of B by a subcategory X is the category with the same objects as B and the space of morphisms from A to B is the quotient of group of morphisms from A to B in B by the subgroup consisting of morphisms factor through objects in X. We use Ab to denote the category of abelian groups.

    In the following, we recall the definition and some properties of extriangulated categories from [4], [11] and [3].

    Suppose there exists a biadditive functor E:Bop×BAb. Let A,CB be two objects, an element δE(C,A) is called an E-extension. Zero element in E(C,A) is called the split E-extension.

    Let s be a correspondence, which associates any E-extension δE(C,A) to an equivalence class s(δ)=[AxByC]. Moreover, if s satisfies the conditions in [3, Definition 2.9], we call it a realization of E.

    Definition 2.1. [3, Definition 2.12] A triplet (B,E,s) is called an externally triangulated category, or for short, extriangulated category if

    (ET1) E:Bop×BAb is a biadditive functor.

    (ET2) s is an additive realization of E.

    (ET3) For a pair of E-extensions δE(C,A) and δE(C,A), realized as s(δ)=[AxByC] and s(δ)=[AxByC]. If there exists a commutative square,

    then there exists a morphism c:CC which makes the above diagram commutative.

    (ET3)op Dual of (ET3).

    (ET4) Let δ and δ be two E-extensions realized by AfBfD and BgCgF, respectively. Then there exist an object EB, and a commutative diagram

    and an E-extension δ realized by AhChE, which satisfy the following compatibilities:

    (i). DdEeF realizes E(F,f)(δ),

    (ii). E(d,A)(δ)=δ,

    (iii). E(E,f)(δ)=E(e,B)(δ).

    (ET4op) Dual of (ET4).

    Let B be an extriangulated category, we recall some notations from [3,6].

    ● We call a sequence XxYyZ a conflation if it realizes some E-extension δE(Z,X), where the morphism x is called an inflation, the morphism y is called an deflation and XxYyZδ is called an E-triangle.

    ●When XxYyZδ is an E-triangle, X is called the CoCone of the deflation y, and denote it by CoCone(y); C is called the Cone of the inflation x, and denote it by Cone(x).

    Remark 2.2. 1) Both inflations and deflations are closed under composition.

    2) We call a subcategory T extension-closed if for any E-triangle XxYyZδ with X, ZT, then YT.

    Denote I by the subcategory of all injective objects of B and P by the subcategory of all projective objects.

    In an extriangulated category having enough projectives and injectives, Liu and Nakaoka [4] defined the higher extension groups as

    Ei+1(X,Y)=E(Ωi(X),Y)=E(X,Σi(Y)) for i0.

    By [3, Corollary 3.5], there exists a useful lemma.

    Lemma 2.3. For a pair of E-triangles LlMmN and DdEeF. If there is a commutative diagram

    f factors through l if and only if h factors through e.

    In this section, B is always an extriangulated category and T is always a cluster tilting subcategory [6, Definition 2.10].

    Let A, BB be two objects, denote by [¯T](A,ΣB) the subset of B(A,ΣB) such that f[¯T](A,ΣB) if we have f: ATΣB where TT and the following commutative diagram

    where I is an injective object of B [10, Definition 3.2].

    Let M and N be two subcategories of B. The notation [¯T](M,Σ(N))=[T](M,Σ(N)) will mean that [¯T](M,ΣN)=[T](M,ΣN) for every object MM and NN.

    Now, we give the definition of T-cluster tilting subcategories.

    Definition 3.1 Let X be a subcategory of B.

    1) [11, Definition 2.14] X is called T-rigid if [¯T](X,ΣX)=[T](X,ΣX);

    2) X is called T-cluster tilting if X is strongly functorially finite in B and X={MC[¯T](X,ΣM)=[T](X,ΣM) and [¯T](M,ΣX)=[T](M,ΣX)}.

    Remark 3.2. 1) Rigid subcategories are always T-rigid by [6, Definition 2.10];

    2) T-cluster tilting subcategories are always T-rigid;

    3) T-cluster tilting subcategories always contain the class of projective objects P and injective objects I.

    Remark 3.3. Since T is a cluster tilting subcategory, XB, there exists a commutative diagram by [6, Remark 2.11] and Definition 2.1((ET4)op), where T1, T2T and h is a left T-approximation of X:

    Hence XB, there always exists an E-triangle

    Ω(T1)fXΩ(T2)X with TiT.

    By Remark 3.2(3), PT and B=CoCone(T,T) by [6, Remark 2.11(1),(2)]. Following from [4, Theorem 3.2], B_=B/T is an abelian category. fB(A,C), denote by f_ the image of f under the natural quotient functor BB_.

    Let Ω(T)=CoCone(P,T), then Ω(T)_ is the subcategory consisting of projective objects of B_ by [4, Theorem 4.10]. Moreover, modΩ(T)_ denotes the category of coherent functors over the category of Ω(T)_ by [4, Fact 4.13].

    Let G: BmodΩ(T)_, MHomB_(,M)Ω(T)_ be the restricted Yoneda functor. Then G is homological, i.e., any E-triangle XYZ in B yields an exact sequence G(X)G(Y)G(Z) in modΩ(T)_. Similar to [9, Theorem 2.8], we obtain a lemma:

    Lemma 3.4. Denote proj(modΩ(T)_) the subcategory of projective objects in modΩ(T)_. Then

    1) G induces an equivalence Ω(T)proj(modΩ(T)_).

    2) For NmodΩ(T)_, there exists a natural isomorphism

    HommodΩ(T)_(G(Ω(T)),N)N(Ω(T)).

    In the following, we investigate the relationship between B and modΩ(T)_ via G more closely.

    Lemma 3.5. Let X be any subcategory of B. Then

    1) any object XX, there is a projective presentation in mod Ω(T)_

    PG(X)1πG(X)PG(X)0G(X)0.

    2) X is a T-rigid subcategory if and only if the class {πG(X)XX} has property ((S) [9, Definition 2.7(1)]).

    Proof. 1). By Remark 3.3, there exists an E-triangle:

    Ω(T1)fXΩ(T0)X

    When we apply the functor G to it, there exists an exact sequence G(Ω(T1))G(Ω(T0))G(X)0. By Lemma 3.4(1), G(Ω(Ti)) is projective in mod Ω(T)_. So the above exact sequence is the desired projective presentation.

    2). For any X0X, using the similar proof to [9, Lemma 4.1], we get the following commutative diagram

    where α=HommodΩT_(πG(X),G(X0)). By Lemma 3.4(2), both the left and right vertical maps are isomorphisms. Hence the set {πG(X)XX} has property ((S) iff α is epic iff HomB_(fX,X0) is epic iff X is a T-rigid subcategory by [10, Lemma 3.6].

    Lemma 3.6. Let X be a T-rigid subcategory and T1 a subcategory of T. Then XT1 is a T-rigid subcategory iff E(T1,X)=0.

    Proof. For any MXT1, then M=XT1 for XX and T1T1. Let h: XT be a left T-approximation of X and y: T1Σ(X) for XX any morphism. Then there exists the following commutative diagram

    with P1P, f=(h001) and β=(i000i1).

    When XT1 is a T-rigid subcategory, we can get a morphism g: XT1Σ(X)Σ(T1) such that βg=(10)y(0 1)f. i.e., b: T1I such that y=i0b. So E(T1,X)=0 and then E(T1,X)=0.

    Let γ=(r11r12r21r22): TT1Σ(X)Σ(T1) be a morphism. As X is T-rigid, r11h: XΣ(X) factors through i0. Since E(T,X)=0, r12: T1Σ(X) factors through i0. As T is rigid, the morphism r21h: XTΣ(T1) factors through i1, and the morphism r22: T1Σ(T1) factors through i1. So the morphism γf can factor through β=(i000i1). Therefore XT1 is an T-rigid subcategory.

    For the definition of τ-rigid pair in an additive category, we refer the readers to see [9, Definition 2.7].

    Lemma 3.7. Let U be a class of T-rigid subcategories and V a class of τ-rigid pairs of modΩ(T)_. Then there exists a bijection φ: UV, given by : X(G(X),Ω(T)Ω(X)).

    Proof. Let X be T-rigid. By Lemma 3.5, G(X) is a τ-rigid subcategory of mod Ω(T)_.

    Let YΩ(T)Ω(X), then there exists X0X such that Y=Ω(X0). Consider the E-triangle Ω(X0)PX0 with PP. XX, applying HomB(,X) yields an exact sequence HomB(P,X)HomB(Ω(X0),X)E(X0,X)0. Hence in B_=B/T, HomB_(Ω(X0),X)E(X0,X).

    By Remark 3.3, for X0, there is an E-triangle Ω(T1)Ω(T2)X0 with T1, T2T. Applying HomB_(,X), we obtain an exact sequence HomB_(Ω(T2),X)HomB_(Ω(T1),X)E(X0,X)E(Ω(T2),X). By [10, Lemma 3.6], HomB_(Ω(T2),X)HomB_(Ω(T1),X) is epic. Moreover, Ω(T2)_ is projective in B_ by [4, Proposition 4.8]. So E(Ω(T2),X)=0. Thus E(X0,X)=0. Hence XX,

    G(X)(Y)=HomB_(Ω(X0),X)=0.

    So (G(X),Ω(T)Ω(X)) is a τ-rigid pairs of modΩ(T)_.

    We will show φ is a surjective map.

    Let (N,σ) be a τ-rigid pair of modΩ(T)_. NN, consider the projective presentation

    P1πNP0N0

    such that the class {πN|NN} has Property (S). By Lemma 3.4, there exists a unique morphism fN: Ω(T1)Ω(T0) in Ω(T)_ satisfying G(fN)=πN and G(Cone(fN))N. Following from Lemma 3.5, X1:= {cone(fN)NN} is a T-rigid subcategory.

    Let X=X1Y, where Y={TTΩ(T)σ}. For any T0Y, there is an E-triangle Ω(T0)PT0 with PP. For any Cone(fN)X1, applying HomB_(,Cone(fN)), yields an exact sequence HomB_(Ω(T0),Cone(fN))E(T0,Cone(fN))E(P,Cone(fN))=0. Since (N,σ) is a τ-rigid pair, HomB_(Ω(T0),Cone(fN))=G(Cone(fM))(Ω(T0))=0. So E(T0,Cone(fN))=0. Due to Lemma 3.6, X=X1Y is T-rigid. Since YT, we get G(Y)=HomB_(,T)Ω(T)=0 by [4, Lemma 4.7]. So G(X)=G(X1)=N.

    It is straightforward to check that Ω(T)Ω(X1)=0. Let XΩ(T)Ω(X), then XΩ(T) and XΩ(X)=Ω(X1)σ. So we can assume that X=Ω(X1)E, where Eσ. Then Ω(X1)EΩ(T). Since EΩ(T), we get Ω(X1)Ω(T)Ω(X1)=0. So Ω(T)Ω(X)σ. Clearly, σΩ(T). Moreover, σΩ(X). So σΩ(T)Ω(X). Hence Ω(T)Ω(X)=σ. Therefore φ is surjective.

    Lastly, φ is injective by the similar proof method to [9, Proposition 4.2].

    Therefore φ is bijective.

    Lemma 3.8. Let T be a rigid subcategory and AaBCδ an E-triangle satisfying [¯T](C,Σ(A))=[T](C,Σ(A)). If there exist an E-extension γE(T,A) and a morphism t: CT with TT such that tγ=δ, then the E-triangle AaBCδ splits.

    Proof. Applying HomB(T,) to the E-triangle AIiΣ(A)α with II, yields an exact sequence HomB(T,A)E(T,X)E(T,I)=0. So there is a morphism dHomB(T,Σ(A)) such that γ=dα. So δ=tγ=tdα=(dt)α. So we have a diagram which is commutative:

    Since [¯T](C,Σ(A))=[T](C,Σ(A)) and dt[T](C,Σ(A)), dt can factor through i. So 1A can factor through a and the result follows.

    Now, we will show our main theorem, which explains the relation between T-cluster tilting subcategories and support τ-tilting pairs of modΩ(T)_.

    The subcategory X is called a preimage of Y by G if G(X)=Y.

    Theorem 3.9. There is a correspondence between the class of T-cluster tilting subcategories of B and the class of support τ-tilting pairs of modΩ(T)_ such that the class of preimages of support τ-tilting subcategories is contravariantly finite in B.

    Proof. Let φ be the bijective map, such that X(G(X),Ω(TΩ(X))), where G is the restricted Yoneda functor defined in the argument above Lemma 3.4.

    1). The map φ is well-defined.

    If Xis T-cluster tilting, then X is T-rigid. So φ(X) is a τ-rigid pair of modΩ(T)_ by Lemma 3.7. Therefore Ω(T)Ω(X)KerG(X). Assume Ω(T0)Ω(T) is an object of KerG(X). Then HomB_(Ω(T0),X)=0. Applying HomB_(,X) with XX to Ω(T0)PT0 with PP, yields an exact sequence

    HomB_(P,X)HomB_(Ω(T),X)E(T0,X)0.

    Hence we get E(T0,X)HomB_(Ω(T0),X)=0.

    Applying HomB(T0,) to XIΣ(X), we obtain

    (3.1)      [¯T](T0,Σ(X))=[T](T0,Σ(X)).

    For any ba: XaRbΣ(T0) with RT, as T is rigid, we get a commutative diagram:

    Hence we get (3.2)[¯T](X,Σ(T0))=[T](X,Σ(T0)).

    By the equalities (3.1) and (3.2) and X being a T-rigid subcategory, we obtain

    [¯T](X,Σ(XT0))=[T](X,Σ(XT0)) and [¯T](XT0,Σ(X))=[T](XT0,Σ(X)).

    As X is T-cluster tilting, we get XT0X. So T0X. And thus Ω(T0)Ω(T)Ω(X). Hence KerG(X)=Ω(T)Ω(X).

    Since X is functorially finte, similar to [6, Lemma 4.1(2)], Ω(T)Ω(T), we can find an E-triangle Ω(T)fX1X2, where X1, X2X and f is a left X-approximation. Applying G, yields an exact sequence

    G(Ω(R))G(f)G(X1)G(X2)0.

    Thus we get a diagram which is commutative, where HomB_(f,X) is surjective.

    By Lemma 3.4, the morphism G(f) is surjective. So G(f) is a left G(X)-approximation and (G(X),Ω(T)Ω(X)) is a support τ-tilting pair of modΩ(T)_ by [3, Definition 2.12].

    2). φ is epic.

    Assume (N,σ) is a support τ-tilting pair of modΩ(T)_. By Lemma 3.7, there is a T-rigid subcategory X satisfies G(X)=N. So Ω(T)Ω((T)), there is an exact sequence G(Ω(T))αG(X3)G(X4)0, such that X3, X4X and α is a left G(X)-approximation. By Yoneda's lemma, we have a unique morphism in modΩ((T))_:

    β: Ω(T)X3 such that α=G(β) and G(cone(β))G(X4).

    Moreover, XX, consider the following commutative diagram

    By Lemma 3.4, G() is surjective. So the map HomB_(β,X) is surjective.

    Denote Cone(β) by YR and Xadd{YRΩ(T)Ω(T)} by ˜X.

    We claim ˜X is T-rigid.

    (I). Assume a: YRa1T0a2Σ(X) with T0T and XX. Consider the following diagram:

    Since X is T-rigid, f: X3I such that aγ=if. So there is a morphism g:Ω(T)X making the upper diagram commutative. Since HomB_(β,X) is surjective, g factors through β. Hence a factors through i, i.e., [¯T](YR,Σ(X))=[T](YR,Σ(X)).

    (II). For any morphism b: Xb1T0b2Σ(YR) with T0T and XX. Consider the following diagram:

    By [3, Lemma 5.9], RΣ(X3)Σ(YT) is an E-triangle. Because T is rigid, b2 factors through γ1. By the fact that X is T-rigid, b=b2b1 can factor through iX. Since γ1iX=iY, we get that b factors through iY. So [¯T](X,Σ(YT))=[T](X,Σ(YT)).

    By (I) and (II), we also obtain [¯T](YT,Σ(YT))=[T](YT,Σ(YT)).

    Therefore ˜X=Xadd{YTΩ(T)Ω(T)} is T-rigid.

    Let MB satisfying [¯T](M,Σ(˜X))=[T](M,Σ(˜X)) and [¯T](˜X,ΣM)=[T](˜X,ΣM). Consider the E-triangle:

    Ω(T5)fΩ(T6)gM

    where T5, T6T. By the above discussion, there exist two E-triangles:

    Ω(T6)uX6vY6 and Ω(T5)uX5vY5.

    where X5, X6X, u and u are left X-approximations of Ω(T6), Ω(T5), respectively. So there exists a diagram of E-triangles which is commutative:

    We claim that the morphism x=uf is a left X-approximation of Ω(T5). In fact, let XX and d: Ω(T5)X, we can get a commutative diagram of E-triangles:

    where PP. By the assumption, [¯T](M,Σ(X))=[T](M,Σ(X)). So d2h factors through iX. By Lemma 2.3, d factors through f. Thus f1: Ω(T6)X such that d=f1f. Moreover, u is a left X-approximation of Ω(T6). So u1: X6X such that f1=u1u. Thus d=f1f=u1uf=u1x. So x=uf is a left X-approximation of Ω(T5).

    Hence there is a commutative diagram:

    By [3, Corollary 3.16], we get an E-triangle X6(yλ)NX5Y5xδ5

    Since u is a left X-approximation of Ω(T5), there is also a commutative diagram with PP:

    such that δ5=tμ. So xδ5=xtμ=txμ. By Lemma 3.8, the E-triangle xδ5 splits. So NX5X6Y5˜X. hence N˜X.

    Similarly, consider the following commutative diagram with PP:

    and the E-triangle MNYgδ6. Then t: YT6 such that δ6=tδ. Then gδ6=gtδ=t(gδ). Since [¯T](˜X,ΣM)=[T](˜X,ΣM), the E-triangle gδ6 splits by Lemma 3.5 and M is a direct summands of N. Hence M˜X.

    By the above, we get ˜X is a T-cluster tilting subcategory.

    By the definition of YR, G(YR)G(X). So G(˜X)G(X)N. Moreover, σ=Ω(T)Ω(X)Ω(T)Ω(˜X) and Ω(T)Ω(˜X)kerG(X)=σ. So Ω(T)Ω(˜X)=σ. Hence φ is surjective.

    3). φ is injective following from the proof of Lemma 3.7.

    By [4, Proposition 4.8 and Fact 4.13], B_modΩ(T)_. So it is easy to get the following corollary by Theorem 3.9:

    Corollary 3.10. Let X be a subcategory of B.

    1) X is T-rigid iff X_ is τ-rigid subcategory of B_.

    2) X is T-cluster tilting iff X_ is support τ-tilting subcategory of B_.

    If let H=CoCone(T,T), then H can completely replace B and draw the corresponding conclusion by the proof Lemma 3.7 and Theorem 3.9, which is exactly [12, Theorem 3.8]. If let B is a triangulated category, then Theorem 3.9 is exactly [9, Theorem 4.3].

    This research was supported by the National Natural Science Foundation of China (No. 12101344) and Shan Dong Provincial Natural Science Foundation of China (No.ZR2015PA001).

    The authors declare they have no conflict of interest.



    [1] P. Harar, J. B. Alonso-Hernandezy, J. Mekyska, Z. Galaz, R. Burget, Z. Smekal, Voice pathology detection using deep learning: a preliminary study, in 2017 international conference and workshop on bioinspired intelligence (IWOBI), (2017), 1-4.
    [2] M. Alhussein, G. Muhammad, Voice pathology detection using deep learning on mobile healthcare framework, IEEE Access, 6 (2018), 41034-41041. doi: 10.1109/ACCESS.2018.2856238
    [3] F. Teixeira, J. Fernandes, V. Guedes, A. Junior, J. P. Teixeira, Classification of control/pathologic subjects with support vector machines, Procedia Comput. Sci., 138 (2018), 272-279. doi: 10.1016/j.procs.2018.10.039
    [4] V. Guedes, A. Junior, J. Fernandes, F. Teixeira, J. P. Teixeira, Long short term memory on chronic laryngitis classification, Procedia Comput. Sci., 138 (2018), 250-257. doi: 10.1016/j.procs.2018.10.036
    [5] J. P. Teixeira, P. O. Fernandes, N. Alves, Vocal acoustic analysis-classification of dysphonic voices with artificial neural networks, Procedia Comput. Sci., 121 (2017), 19-26. doi: 10.1016/j.procs.2017.11.004
    [6] J. Kreiman, B. R. Gerratt, K. Precoda, Listener experience and perception of voice quality, J. Speech, Lang., Hear. Res., 33 (1990), 103-115. doi: 10.1044/jshr.3301.103
    [7] G. Muhammad, G. Altuwaijri, M. Alsulaiman, Z. Ali, T. A. Mesallam, M. Farahat, et al., Automatic voice pathology detection and classification using vocal tract area irregularity, Biocybern. Biomed. Eng., 36 (2016), 309-317. doi: 10.1016/j.bbe.2016.01.004
    [8] N. Rezaei, A. Salehi, An introduction to speech sciences (acoustic analysis of speech), Iran. Rehabil. J., 4 (2006), 5-14.
    [9] J. W. Lee, H. G. Kang, J. Y. Choi, Y. I. Son, An investigation of vocal tract characteristics for acoustic discrimination of pathological voices, BioMed Res. Int., 2013 (2013).
    [10] US Department of Health and Human Services, NIDCD fact sheet: Speech and language developmental milestones, NIH Publication, 2010.
    [11] S. A. Syed, M. Rashid, S. Hussain, Meta-analysis of voice disorders databases and applied machine learning techniques, Math. Biosci. Eng., 17 (2020), 7958-7979. doi: 10.3934/mbe.2020404
    [12] B. Boyanov, S. Hadjitodorov, Acoustic analysis of pathological voices. A voice analysis system for the screening of laryngeal diseases, IEEE Eng. Med. Biol. Mag., 16 (1997), 74-82.
    [13] A. Zulfiqar, A. Muhammad, A. M. Enriquez, A speaker identification system using MFCC features with VQ technique, in 2009 Third International Symposium on Intelligent Information Technology Application, IEEE, 3 (2009), 115-118.
    [14] A. Al-Nasheri, G. Muhammad, M. Alsulaiman, Z. Ali, K. H. Malki, T. A. Mesallam, et al., Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions, IEEE Access, 6 (2017), 6961-6974.
    [15] A. Al-Nasheri, G. Muhammad, M. Alsulaiman, Z. Ali, T. A. Mesallam, M. Farahat, et al., An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification, J. Voice, 31 (2017), 113.e9-e18.
    [16] A. Al-Nasheri, G. Muhammad, M. Alsulaiman, Z. Ali, Investigation of voice pathology detection and classification on different frequency regions using correlation functions, J. Voice, 31 (2017), 3-15. doi: 10.1016/j.jvoice.2016.01.014
    [17] F. Teixeira, J. Fernandes, V. Guedes, A. Junior, J. P. Teixeira, Classification of control/pathologic subjects with support vector machines, Proced. Comput. Sci., 138 (2018), 272-279. doi: 10.1016/j.procs.2018.10.039
    [18] J. P. Teixeira, P. O. Fernandes, N. Alves, Vocal acoustic analysis-classification of dysphonic voices with artificial neural networks, Proced. Comput. Sci., 121 (2017), 19-26. doi: 10.1016/j.procs.2017.11.004
    [19] E. S. Fonseca, R. C. Guido, S. B. Junior, H. Dezani, R. R. Gati, D. C. Pereira, Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM), Biomed. Signal Process. Control, 55 (2020).
    [20] Z. Ali, M. Alsulaiman, G. Muhammad, I. Elamvazuthi, A. Al-nasheri, T. A. Mesallam, et al., Intra-and inter-database study for Arabic, English, and German databases: do conventional speech features detect voice pathology?, J. Voice, 31 (2017), 386.e1-e8.
    [21] S. Kadiri, P. Alku, Analysis and detection of pathological voice using glottal source features, IEEE J. Sel. Top. Signal Process., 14 (2019), 367-379.
    [22] B. Woldert-Jokisz, Saarbruecken Voice Database, 2007. Available from: http://www.stimmdatenbank.coli.uni-saarland.de/help_en.php4.
    [23] S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, W. Xu, Applications of support vector machine (SVM) learning in cancer genomics, Cancer Genomics-Proteomics, 15 (2018), 41-51.
    [24] A. Shmilovici, Support vector machines, in Data Mining and Knowledge Discovery Handbook, Springer, Boston, MA, (2009), 231-247.
    [25] W. Zhang, F. Gao, An improvement to naive bayes for text classification, Procedia Eng., 15 (2011), 2160-2164. doi: 10.1016/j.proeng.2011.08.404
    [26] C. C. Aggarwal, Data Mining: The Textbook, Springer, 2015.
    [27] L. Toth, A. Kocsor, J. Csirik, On naive Bayes in speech recognition, Int. J. Appl. Math. Comput. Sci., 15 (2005), 287-294.
    [28] C. Kingsford, S. Salzberg, What are decision trees?, Nat. Biotechnol., 26 (2008), 1011-1013. doi: 10.1038/nbt0908-1011
    [29] T. G. Dietterich, Ensemble methods in machine learning, in International workshop on multiple classifier systems, Springer, Berlin, Heidelberg, (2000), 1-15.
    [30] E. Bauer, R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Mach. Learn., 36 (1999), 105-139. doi: 10.1023/A:1007515423169
    [31] R. Sharma, K. Hara, H. Hirayama, A machine learning and cross-validation approach for the discrimination of vegetation physiognomic types using satellite based multispectral and multitemporal data, Scientifica, 2017 (2017), 9806479.
    [32] R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition, Wiley-Interscience, USA, 2000.
    [33] S. Memon, M. Lech, L. He, Using information theoretic vector quantization for inverted MFCC based speaker verification, in 2009 2nd International Conference on Computer, Control and Communication, IEEE, (2009), 1-5.
    [34] M. Sahidullah, G. Saha, On the use of distributed dct in speaker identification, in 2009 Annual IEEE India Conference, IEEE, (2009), 1-4.
    [35] Ö. Eskidere, A. Gürhanlı, Voice disorder classification based on multitaper mel frequency cepstral coefficients features, Comput. Math. Methods Med., 2015 (2015), 956249.
    [36] P. Mahesha, D. Vinod, Classification of speech dysfluencies using speech parameterization techniques and multiclass SVM, in International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Springer, Berlin, Heidelberg, (2013), 298-308.
    [37] M. M. Oo, Comparative study of MFCC feature with different machine learning techniques in acoustic scene classification, Int. J. Res. Eng., 5 (2018), 439-444.
    [38] A. Mehler, S. Sharoff, M. Santini, Genres on the Web: Computational Models and Empirical Studies, Springer Science & Business Media, 2010.
    [39] K. Prahallad, Speech technology: A practical introduction, topic: Spectrogram, cepstrum and mel-frequency analysis, Carnegie Mellon Univ. Int. Inst. Inf. Technol. Hyderabad, Slide, 2011.
  • This article has been cited by:

    1. Zhen Zhang, Shance Wang, Relative subcategories with respect to a rigid subcategory, 2025, 0092-7872, 1, 10.1080/00927872.2025.2509823
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4352) PDF downloads(173) Cited by(21)

Figures and Tables

Figures(6)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog