Enclosure including an electronic wall detection device

Abstract

Enclosure comprising an electronic wall detection device. The present invention relates to an enclosure (10) comprising: at least one loudspeaker (20) for emitting an audio signal, several microphones (25) for acquiring a received audio signal, whether a wall (15) is present in the enclosure's environment, the received audio signal including the audio signal reflected from the wall, and an electronic wall detection device (30) comprising a first computing module (35) for calculating a spectrogram of the arrival directions of the received audio signal. The detection device is characterized in that it further comprises: a second computing module (40) for calculating an energy level of the audio signal received from each microphone, and a determination module (45) for determining the presence or absence of the wall in the enclosure's environment using a neural network model. Figure for the abstract: Figure 1

Description

Title of the invention: Enclosure comprising an electronic device for detecting a wall

[0001] The present invention relates to a speaker comprising at least one loudspeaker capable of emitting an emitted audio signal, and several microphones each capable of acquiring an audio signal received by the speaker.

[0002] The present invention relates to the field of loudspeakers, preferably portable loudspeakers, also called nomadic loudspeakers.

[0003] Such enclosures are generally suitable for being positioned in different environments having different structures.

[0004] However, the acoustics of the sounds emitted by a speaker are strongly influenced by the environment, and in particular the presence of obstacles such as walls. Indeed, when the speaker is positioned close to a wall, for example less than 50 cm from the latter, the sounds emitted are reflected on the wall and propagate to a user with a delay compared to the sounds following a direct path between the speaker and the user.

[0005] This leads to a degraded experience for the user of the speaker.

[0006] There is therefore a need to determine the location of the walls in the environment of the acoustic enclosure in order to be able to adapt the emission of signals by the enclosure to limit the degradation of the user experience.

[0007] To this end, the invention relates to an enclosure further comprising at least one loudspeaker capable of emitting an emitted audio signal, - several microphones each capable of acquiring a received audio signal, if a wall is present in an environment of the enclosure, the received audio signal comprising the emitted audio signal which has been reflected on the wall, and - an electronic wall detection device connected to each microphone,

[0008] the electronic detection device comprising: • a first calculation module capable of calculating a spectrogram of the directions of arrival of a component of the audio signal received by the speaker, from the received audio signal acquired by each of the microphones,

[0009] characterized in that the electronic detection device further comprises: • a second calculation module capable of calculating an energy level of the received audio signal acquired from each microphone, and • a determination module capable of determining the presence of the wall or the absence of a wall in the environment of the enclosure by applying a neural network model(s) to the spectrogram of the directions of arrival and at the determined energy levels.

[0010] According to particular embodiments, the enclosure according to the invention comprises one or more of the following characteristics, taken in isolation, or according to all technically possible combinations: - the neural network(s) model comprises a first convolutional neural network suitable for receiving the spectrogram of the arrival directions, a second neural network suitable for receiving the energy levels, a concatenation block suitable for concatenating data from the first and second neural networks to form concatenated data, and a third neural network suitable for processing the concatenated data to determine the presence or absence of the wall, - the first neural network comprises a circular sliding window and at least one layer of neurons, the sliding window being suitable for being applied to the spectrogram of directions of arrival before the layer of neurons, - the neural network model is capable of, if the wall is present in the environment of the enclosure, determining a position of the wall relative to the enclosure from among a plurality of predefined positions, from the spectrogram of the directions of arrival and the calculated energy levels, - at least one loudspeaker is capable of emitting the audio signal emitted according to a speaker diffusion mode,

[0011] the electronic detection device further comprising an adaptation module capable of reconfiguring the diffusion mode of the speaker as a function of the determination of the presence, or absence, of the wall, - the spectrogram of directions of arrival is a matrix comprising a plurality of values, each value corresponding to the power of the audio signal received by the speaker according to a predefined angular interval relative to a predefined reference point centered on a center of the speaker, and according to a predefined frequency interval, - the emitted audio signal is included in an audio stream originating from a broadcast instruction from a user; and - the enclosure further comprises an accelerometer capable of determining whether the enclosure is stationary and of issuing a calculation instruction when the enclosure is stationary, the first and second calculation modules being capable of calculating the spectrogram of the directions of arrival and the energy levels following receipt of the calculation instruction from the accelerometer.

[0012] The invention also relates to a method for detecting a wall in an environment of an enclosure comprising at least one loudspeaker, several mi microphones and an electronic wall detection device connected to each microphone,

[0013] the method comprising the following steps: - emission of an audio signal emitted by at least one loudspeaker, - acquisition, by each microphone, of a received audio signal, if the wall is present in an environment of the enclosure, the received audio signal including the audio signal which has been reflected on the wall, - calculation of a spectrogram of the directions of arrival of a component of the audio signal received by the speaker, from the received audio signal acquired by each microphone,

[0014] characterized in that the method further comprises the following steps: - calculation of an energy level of the received audio signal acquired from each microphone, and - determination of the presence of the wall or the absence of a wall in the environment of the enclosure by applying a neural network model to the spectrogram of the directions of arrival and the determined energy levels.

[0015] The invention also relates to a computer program product comprising software instructions which, when executed by a computer, implement a detection method as described above.

[0016] The invention will appear more clearly on reading the description which follows, given solely by way of non-limiting example, and made with reference to the drawings in which: - [Fig.l] [Fig.l] is a schematic representation of an enclosure according to the invention; - [Fig.2] [Fig.2] is an example of a spectrogram of the arrival directions suitable for being determined by an electronic detection device included within the enclosure of [Fig.l]; - [Fig.3] [Fig.3] is an example of energy levels suitable for being determined by the electronic detection device included in the enclosure of [Fig.l]; - [Fig.4] [Fig.4] is a schematic representation of a network(s) model of neurons implemented by the electronic detection device included in [Fig.l]; and - [Fig.5] [Fig.5] is a flowchart of a detection method implemented by the enclosure of [Fig.l].

[0017] In [Fig.l] an enclosure 10 is shown in an environment. The environment includes a wall 15 near the enclosure 10. The term "at proximity”, a distance less than or equal to 1m and preferably less than or equal to 50cm.

[0018] The enclosure 10 comprises at least one loudspeaker 20 capable of emitting audio signals. Preferably, the enclosure 10 comprises several loudspeakers 20, preferably distributed around the periphery of the enclosure 10 to emit audio signals in several directions.

[0019] In the example of [Fig. 1], the enclosure 20 comprises two loudspeakers 20.

[0020] The enclosure 10 further comprises a plurality of microphones 25, each capable of acquiring the audio signals present in the environment of the enclosure 10. In the example of [Fig.l], the enclosure 10 comprises four microphones distributed over the outer surface of the enclosure 10. The microphones 25 are preferably angularly regularly spaced from one another. Thus, in the example where the enclosure 10 comprises four microphones 25, the microphones 25 are angularly distributed according to an angular displacement equal to 90° so as to acquire audio signals in directions that are as spaced apart from one another as possible.

[0021] The speaker 10 further comprises a control device 27, an accelerometer 28 and an electronic detection device 30. The control device 27 is connected to the speakers 20 and optionally to a source of audio content (not shown), such as a digital player, an optical disc player, a vinyl record player or a smartphone. The control device 27 is capable of receiving audio content from the source, and of sending an excitation command to the speakers 20 to broadcast the received audio content. The excitation command then causes the emission of an audio signal by the speakers 20, called the emitted audio signal. Preferably, the control device 27 is capable of sending the excitation command according to a first broadcast mode of the speaker 10 chosen from several predefined broadcast modes of the speaker 10.For example, the chosen diffusion mode is a neutral mode in which the loudspeakers 20 are controlled synchronously.

[0022] It is then understood that the emitted audio signal is included in an audio stream originating from a broadcast instruction from a user. In other words, the emitted audio signal is for example any type of music or voice, and is not limited to a predefined test signal.

[0023] If the wall 15 is present in the environment of the enclosure 10, the emitted audio signal is at least partially reflected on the wall 15.

[0024] Each microphone 25 is then capable of acquiring an audio signal received by the speaker 10 following the emission of the audio signal emitted by the loudspeakers 20.

[0025] The received audio signal comprises a direct component which is the audio signal transmitted along the direct path between the loudspeakers 20 and the microphones 25.

[0026] If the wall 15 is present in the environment of the enclosure 10, as in the example of [Fig.l], the received audio signal further comprises a reflected component which is the audio signal emitted and reflected on the wall 15.

[0027] Optionally, the received audio signal further includes noise. In other words, the received audio signal includes: - the direct component and the noise, if no wall 15 is present in the environment of the enclosure 10; or - the direct component, the reflected component, and the noise, if the wall 15 is present in the environment of the enclosure 10.

[0028] The accelerometer 28 is connected to the detection device 30. The accelerometer 28 is capable of measuring an acceleration of the enclosure 10 and of sending, to the detection device 30, a calculation instruction when the enclosure 10 is newly stationary.

[0029] The electronic detection device 30 comprises a first module 35 for calculating a spectrogram of the directions of arrival, a second module 40 for calculating energy levels, a module 45 for determining the presence or absence of the wall 15, and optionally a module 50 for adapting a diffusion mode.

[0030] In the example of [Fig.l], the electronic detection device 30 is a computer formed for example of a memory 55 and a processor 60 associated with the memory 55.

[0031] In the example of [Fig.l], the first calculation module 35, the second calculation module 40 and the determination module 45, as well as, as an optional complement, the adaptation module 50, are each produced in the form of software, or a software brick, executable by the processor. The memory 55 of the electronic detection device 25 is then capable of storing a first calculation software, a second calculation software, and a determination software, as well as, as an optional complement, an adaptation software. The processor is then capable of executing each of the software among the first calculation software, the second calculation software, and the detection software, as well as, as an optional complement, the adaptation software.

[0032] In a variant not shown, the first calculation module 35, the second calculation module 40 and the determination module 45, as well as the optional addition the adaptation module 50, are each produced in the form of a programmable logic component, such as an FPGA (Field Programmable Gate Array), or an integrated circuit, such as an ASIC (Application Specific Integrated Circuit).

[0033] When the electronic detection device 30 is produced in the form of one or more software programs, that is to say in the form of a computer program, also called a computer program product, it is furthermore capable of being recorded on a support, not shown, readable by computer. The computer-readable medium is, for example, a medium capable of storing electronic instructions and of being coupled to a bus of a computer system. For example, the readable medium is an optical disc, a magneto-optical disc, a ROM memory, a RAM memory, any type of non-volatile memory (for example FLASH or NVRAM) or a magnetic card. A computer program comprising software instructions is then stored on the readable medium.

[0034] The control device 27 is for example also a computer comprising a memory (not shown) and a processor (not shown).

[0035] Alternatively, the detection device 30 and the control device 27 form a single computer comprising a single processor 60 and a single memory 55. The memory 55 then also stores control software.

[0036] The first calculation module 35 is capable of acquiring the audio signal received from each microphone 25, for example following receipt of the calculation instruction from the accelerometer 28. The first calculation module 35 is capable of calculating a spectrogram of the directions of arrival on the speaker 10 of the received signal, from the audio signal received by the speaker 10 and acquired by each of the microphones 25. The spectrogram of the directions of arrival is calculated from all of the components of the received audio signal.

[0037] For this purpose, the first calculation module 35 is for example capable of applying the MUSIC (Multiple Signal Classification) algorithm known from the state of the art and in particular described on the following internet page https: / / fr.wikipedia.org / wiki / MUSIC_(algorithme). In a manner known per se, the MUSIC algorithm is configured to detect a sound signal coming from a search direction. When it detects a sound signal coming from a particular direction, it is necessarily the reflected component because, by construction, the direct component arrives identically from all directions.

[0038] The use of the MUSIC algorithm then makes it possible to identify a preferred direction of arrival for the received audio signal.

[0039] If the wall 15 is present in the environment of the enclosure 10, the identified component corresponds substantially to the reflected component of the received audio signal.

[0040] The MUSIC algorithm provides a matrix.

[0041] The columns of this matrix represent the directions of arrival around the enclosure 10 according to a first sampling of the directions of arrival. The columns therefore correspond to an angular interval of identical width from one column to another, in a predefined reference frame and centered on a center of the enclosure 10.

[0042] The rows of this matrix correspond to frequency intervals (from English, frequency bin). For example, the matrix has 257 rows.

[0043] Each value of the matrix is ​​associated with a column and a row, and corresponds to the power of the component identified by the MUSIC algorithm, in the angular interval of the associated column, and for the frequency interval of the associated row.

[0044] The first calculation module 35 is further optionally capable of performing frequency sub-sampling and smoothing in sixth octaves on the frequency axis of the matrix to reduce the number of lines, for example to 25 lines. Alternatively, the smoothing is for example performed in twelfth octaves, in third octaves or by octave.

[0045] Furthermore, the first calculation module 35 is optionally capable of reducing the number of columns by averaging the values ​​over several angular intervals, for example so that the matrix only comprises 8 columns. In other words, after the average is carried out, each column corresponds to an angular interval of 45°.

[0046] In addition, the first calculation module 35 is preferably capable of normalizing the values ​​of the matrix obtained so that the highest value corresponds to the value 1 and the other values ​​correspond to percentages of the highest value.

[0047] Thus, the direction(s) of arrival for which the values ​​are the highest are the most probable directions of arrival on the enclosure 10 of the reflected component. Thus, this or these directions of arrival are the most probable directions in which the wall 15 is located relative to the enclosure 10.

[0048] The matrix obtained by the first calculation module 35 is then the spectrogram of the directions of arrival of the sound sources around the enclosure 10, including in particular the reflected component identified when it is present. An example of such a spectrogram is illustrated in [Fig.2] in gray levels. In [Fig.2], each box corresponds to a value of the matrix obtained.

[0049] In [Fig. 2], the lighter a box is, the higher the power on the enclosure 10 of the identified reflected component is in the corresponding frequency interval and angular interval. On the contrary, the darker a box is, the lower the power of the identified reflected component is in the corresponding frequency interval and angular interval.

[0050] In [Fig.2], two zones 65 are represented. The zones 65 correspond to sets of frequency intervals for which the power of the identified reflected component is the highest.

[0051] In the example of [Fig.2], these zones correspond to the angular intervals between 90° and 135° on the one hand, and between 270° and 315° on the other hand. There is therefore an ambiguity of 180° concerning the direction of the wall 15 relative to the enclosure 10. This ambiguity results from the fact that the MUSIC algorithm is intended to calculate a spectrogram of the arrival directions in the context of a point source and not of a reflection of an audio signal on a surface formed by the wall 15.

[0052] Furthermore, the material of the wall 15 influences the reflection of the emitted audio signal in terms of frequency. Indeed, depending on the material of the wall 15, certain frequencies are mainly absorbed while others are mainly reflected.

[0053] The second calculation module 40 is also capable of receiving, from each microphone 25, the audio signal received, for example following the reception of the calculation instruction from the accelerometer 28.

[0054] The second calculation module 40 is capable of calculating an energy level of the received audio signal, acquired from each microphone 25.

[0055] For this purpose, the second calculation module 40 is capable of calculating the energy level of the received audio signal, acquired by each microphone 25 for a predefined duration, for example 5 seconds. Preferably, the calculation module 40 is capable of calculating the energy levels only over a predefined frequency band, for example between 20 Hz and 20,000 Hz.

[0056] For example, the energy level is calculated using the following formula:

[0057] r _ yyr ï ^i- .j ~~~7—TV JJH mmv lf -f V max J minJ

[0058] where: E: is the energy level received by microphone i,

[0059] T is the predefined duration,

[0060] f min ct are respectively the minimum and maximum frequencies of the band of frequency,

[0061] f) is the audio signal received by microphone i at time 1 and at frequency / , in the frequency domain, and

[0062] conjÇ) is the complex conjugate function.

[0063] The second calculation module 40 is capable of normalizing the energy levels so that the highest energy level is equal to 1 and the other energy levels are equal to percentages of the highest energy level.

[0064] [Fig. 3] represents an example of calculated energy levels. It is visible in [Fig. 3] that the first microphone 25 acquires the received audio signal with the highest energy level, i.e. a normalized level equal to 1. It is also visible that the other microphones 25 acquire the received audio signal with a lower energy level, i.e. a normalized level between 0 and 1.

[0065] Again with reference to [Fig. 1], the determination module 45 is connected to the first 35 and second 40 calculation modules.

[0066] The determination module 45 is capable of determining the presence of the wall 15 or the absence of the wall 15 in the environment of the enclosure 10 by applying a neural network model 70 to the spectrogram of the directions of arrival and to the determined energy levels.

[0067] [Fig.4] illustrates an architecture of the neural network model 70 implemented by the determination module 45.

[0068] The neural network model 70 comprises a first convolutional neural network 75 suitable for receiving the spectrogram of the arrival directions, a second neural network 80 suitable for receiving the energy levels, a concatenation block 83 intended to concatenate data from the first 75 and second 80 neural networks to form concatenated data, and a third neural network 85 suitable for processing the concatenated data to determine the presence or absence of the wall 15.

[0069] Each neural network comprises an ordered succession of layers of neurons, each of which takes its inputs from the outputs of the previous layer.

[0070] More precisely, each layer comprises neurons taking their inputs from the outputs of the neurons of the previous layer, or from the input variables for the first layer.

[0071] Each neuron is also associated with an operation, i.e. a type of processing, to be carried out by said neuron within the corresponding processing layer.

[0072] Each layer is connected to the other layers by a plurality of synapses. A synaptic weight is associated with each synapse, and each synapse forms a connection between two neurons. Each synaptic weight is preferably a real number, which takes both positive and negative values. In some cases, each synaptic weight is a complex number.

[0073] Each neuron is capable of performing a weighted sum of the value(s) received from the neurons of the previous layer, each value then being multiplied by the respective synaptic weight of each synapse, or link, between said neuron and the neurons of the previous layer, then applying an activation function, typically a non-linear function, to said weighted sum, and delivering at the output of said neuron, in particular to the neurons of the following layer connected to it, the value resulting from the application of the activation function. The activation function makes it possible to introduce non-linearity into the processing performed by each neuron. The sigmoid function, the hyperbolic tangent function, the Heaviside function, the Rectified Linear Unit function also called ReLU function (from the English, Rectified Linear Unit), or the softmax function, are examples of activation functions.

[0074] As an optional addition, each neuron is also capable of applying, in addition, a multiplicative factor, also called bias, to the output of the activation function, and the value delivered at the output of said neuron is then the product of the bias value and the value from the activation function.

[0075] A convolutional neural network is also sometimes referred to as a convolutional neural network or by the acronym CNN (from the English, Convolutional Neural Networks).

[0076] In a convolutional neural network, each neuron in the same layer has exactly the same connection pattern as its neighboring neurons, but at different input positions. The connection pattern is called a convolution kernel or, more often, a "kernel" in reference to the corresponding English term.

[0077] A fully connected layer of neurons is a layer in which the neurons of said layer are each connected to all the neurons of the previous layer.

[0078] Such a type of layer is more often referred to as “fully connected” and sometimes referred to as “dense layer”.

[0079] The first neural network 75 comprises a circular sliding window (circular padding) and at least one layer of neurons. Preferably, the first neural network 75 comprises three layers of neurons.

[0080] The sliding window comprises a kernel of width M and height N, M being less than the number of columns of the matrix forming the spectrogram, N being less than the number of rows of said matrix. The window is capable of moving in the spectrogram to form sub-matrices of dimensions M by N. The sub-matrices are provided to the at least one layer of neurons.

[0081] The circular type of the sliding window is such that the window is capable of forming sub-matrices comprising values ​​from the first column and the last column. In other words, the sliding window moves on the spectrogram as if the first column of the corresponding matrix were contiguous with the last column, i.e. as if the spectrogram formed a cylinder.

[0082] The sliding window is suitable for being applied to the spectrogram of arrival directions, calculated by the first calculation module 35, before the or each layer of neurons.

[0083] Thus, the spectrogram of the arrival directions is processed as an image by the first neural network 75.

[0084] The first neural network 75 provides, at its output, first data.

[0085] The second neural network 80 is a fully connected neural network. The second neural network 80 is capable of taking as input a vector comprising the normalized energy level of each microphone 25 calculated by the second calculation module 40.

[0086] The second neural network 80 provides second data at its output.

[0087] The concatenation block 83 is connected to the output of the first 75 and second 80 neural networks and is capable of concatenating the first and second data into a single vector to form concatenated data.

[0088] The third neural network 85 is connected to the concatenation block 83. The third neural network 85 is for example of the fully connected type.

[0089] The third neural network 85 is preferably capable of classifying the concatenated data among a plurality of classes, for example nine classes.

[0090] For example, each class except one corresponds to a predefined position of the wall 15 relative to the enclosure 10, the last class corresponding to the absence of wall 15. The position of the wall 15 is defined here as the direction in which the wall 15 is located relative to the enclosure 10 in a predefined reference frame centered on the enclosure 10. Thus, each class corresponds to a predefined position of the wall 15, defined by an angular interval in which the wall 15 is located relative to the enclosure 10.

[0091] If the plurality of classes comprises nine classes, each of the first eight classes corresponds to an angular interval of 45°, the ninth class corresponding to the absence of wall 15.

[0092] For this purpose, the third neural network 85 is capable of providing at its output an output vector comprising nine components. The activation function of the neurons of the last layer of the third neural network 85 is preferably the softmax function. Thus, the value of each component of the output vector is between 0 and 1, and the sum of the values ​​of all the components of said vector is equal to 1. The value of each component is then a probability of belonging to the corresponding class.

[0093] The determination module 45 is for example capable of determining the absence of the wall 15 if the component of the output vector having the highest value is the component associated with the class corresponding to the absence of the wall 15. The determination module 45 is then capable of determining the presence of the wall 15 otherwise.

[0094] As an optional addition, the determination module 45 is further capable of determining the position of the wall 15 as being the position defined by the class whose output component has the highest value in the output vector.

[0095] Optionally, the determination module 45 is configured to compare said highest value to a predefined threshold. If the highest value is lower than the predefined threshold then the determination module 45 is able to determine that the detection of the wall 15 is not conclusive and to command the reiteration of the calculations by the first 35 and second 40 calculation modules from second audio signals acquired at a later time.

[0096] The adaptation module 50 is capable of receiving the position of the wall 15, or information on the absence of the wall 15, from the determination module 45.

[0097] The adaptation module 50 is capable of reconfiguring the diffusion mode of the enclosure 10 as a function of the determination of the presence or absence of the wall 15, and more preferably as a function of the position of said wall 15 where appropriate.

[0098] In the example of [Fig.l], a diffusion mode is associated with each of the following positions of the wall 15: opposite one of the loudspeakers 20, opposite the other loudspeaker 20, at 90° from a line joining the two loudspeakers 20 in one direction, and at 90° from the line joining the two loudspeakers 20 in the opposite direction. Thus, four diffusion modes are defined by these positions.

[0099] For each of these four diffusion modes, a delay is for example applied by the control device 27 to one of the loudspeakers 20 to compensate for the presence of the wall 15.

[0100] A fifth diffusion mode is defined as a neutral diffusion mode in which the loudspeakers 20 are controlled synchronously, i.e. without delay. The adaptation module 50 is for example capable of associating the fifth diffusion mode with all the other positions and with the determination of the absence of wall 15.

[0101] In the fifth diffusion mode, an equalization of the different channels, in phase and in amplitude, is preferentially carried out.

[0102] In the first, second, third and fourth diffusion modes, other treatments are also preferably carried out to further improve the sound rendering for a user located opposite the wall with respect to the speaker 10. For example, these treatments include phase and amplitude equalization, and a decomposition of the signal into components to be diffused at the front and at the rear of the speaker 10.

[0103] The adaptation module 50 is adapted to send the reconfigured broadcast mode to the control device 27 so that it controls the loudspeakers 20 in accordance with this reconfigured broadcast mode.

[0104] The operation of the enclosure 10 will now be described with reference to [Fig.5] illustrating a flowchart of a method for determining the presence or absence of the wall 15.

[0105] During a transmission step 110, the control device 27 receives audio content from the source, and sends an excitation command to the speakers 20 to transmit the audio content.

[0106] At a time, the accelerometer 28 detects that the speaker has become immobile following a movement. The accelerometer then sends, to the detection device 30, an instruction indicating that the speaker 10 has become immobile. The audio content emitted by the loudspeakers 20 from this time is the emitted audio signal.

[0107] During an acquisition step 120, each microphone 25 acquires the received audio signal.

[0108] Then, during a first calculation step 130, the first calculation module 35 calculates the spectrogram of the directions of arrival of the component of the audio signal received by the speaker 10, from the received audio signal, acquired by each of the microphones 25, for example as described previously.

[0109] During a second calculation step 140, the second calculation module 40 calculates the energy level of the received audio signal, acquired from each microphone 25, for example as explained previously.

[0110] Preferably, the first 130 and the second 140 calculation steps are implemented simultaneously.

[0111] Then, during a determination step 150, the determination module 45 determines the presence of the wall 15 or the absence of the wall 15 in the environment of the enclosure 10, by applying the neural network model 70 to the spectrogram of the directions of arrival and to the calculated energy levels.

[0112] For example, the determination module 45 applies the first neural network 75 to the spectrogram to obtain the first data and the second neural network 80 to the energy levels to obtain the second data. The determination module 45 then applies the concatenation block 83 to the first and second data to form the concatenated data. The determination module 45 then provides the concatenated data to the third neural network 85 to determine the output vector.

[0113] According to this optional addition, the determination module 45 determines the position of the wall 15 relative to the enclosure 10 or the absence of wall 15, as indicated previously, from the output vector.

[0114] Optionally, during an adaptation step 160, the adaptation module 50 reconfigures the diffusion mode of the enclosure 10 as a function of the position of the wall 15 relative to the enclosure 10, or the absence of a wall 15, for example by sending the reconfigured diffusion mode to the control device 27.

[0115] With the enclosure 10 according to the invention, the presence of the wall 15, or its absence, is detected precisely.

Claims

Claims

1. Enclosure (10) comprising: - at least one loudspeaker (20) capable of emitting a transmitted audio signal, - several microphones (25) each capable of acquiring a received audio signal, if a wall (15) is present in an environment of the enclosure (10), the received audio signal comprising the transmitted audio signal which has been reflected on the wall (15), and - an electronic detection device (30) of the wall (15) connected to each microphone, the electronic detection device comprising: • a first calculation module (35) capable of calculating a spectrogram of the directions of arrival of the audio signal received by the enclosure (10), from the received audio signal acquired by each of the microphones (25), characterized in that the electronic detection device further comprises: • a second calculation module (40) capable of calculating an energy level of the received audio signal acquired from each microphone (25),and • a determination module (45) capable of determining the presence of the wall (15) or the absence of the wall (15) in the environment of the enclosure (10) by applying a neural network model (70) to the spectrogram of the directions of arrival and the determined energy levels.,

2. Enclosure (10) according to the preceding claim, in which the neural network(s) model (70) comprises a first convolutional neural network (75) capable of receiving the spectrogram of the arrival directions, a second neural network (80) capable of receiving the energy levels, a concatenation block (83) capable of concatenating data from the first (75) and second (80) neural networks neurons to form concatenated data, and a third neural network (85) and capable of processing the concatenated data to determine the presence or absence of the wall (15).

3. Enclosure (10) according to claim 2, in which the first neural network (75) comprises a circular sliding window and at least one layer of neurons, the sliding window being suitable for being applied to the spectrogram of directions of arrival before the layer of neurons.

4. Enclosure (10) according to any one of the preceding claims, in which the neural network model (70) is capable of, if the wall (15) is present in the environment of the enclosure (10), determining a position of the wall (15) relative to the enclosure (10) from among a plurality of predefined positions, from the spectrogram of the directions of arrival and the calculated energy levels.

5. Enclosure (10) according to any one of the preceding claims, in which the at least one loudspeaker (20) is capable of emitting the audio signal emitted according to a diffusion mode of the enclosure (10), the electronic detection device (30) further comprising an adaptation module (50) capable of reconfiguring the diffusion mode of the enclosure (10) as a function of the determination of the presence, or absence, of the wall (15).

6. An enclosure (10) according to any preceding claim, wherein the arrival direction spectrogram is a matrix comprising a plurality of values, each value corresponding to the power of the audio signal received by the enclosure (10) according to a predefined angular interval relative to a predefined reference frame centered on a center of the enclosure (10), and according to a predefined frequency interval.

7. An enclosure (10) according to any preceding claim, wherein the emitted audio signal is included in an audio stream originating from a broadcast instruction from a user.

8. An enclosure (10) according to any one of the preceding claims, further comprising an accelerometer (28) capable of determining whether the enclosure (10) is stationary and of issuing a calculation instruction when the enclosure (10) is stationary, the first (35) and second (40) calculation modules being capable of calculating the spectrogram of the directions of arrival and the energy levels following receipt of the calculation instruction from the accelerometer (28).

9. A method of detecting a wall (15) in an environment of a enclosure (10) comprising at least one loudspeaker (20), several microphones (25) and an electronic device (30) for detecting the wall (15) connected to each microphone (25), the method comprising the following steps: - emission (110) of an audio signal emitted by the at least one loudspeaker (20), - acquisition (120), by each microphone (25), of a received audio signal, if the wall (15) is present in an environment of the enclosure (10), the received audio signal comprising the audio signal which has been reflected on the wall (15), - calculation (130) of a spectrogram of the directions of arrival of the audio signal received by the speaker (10), from the received audio signal acquired by each microphone (25), characterized in that the method further comprises the following steps: - calculating (140) an energy level of the received audio signal acquired from each microphone (25), and - determination of the presence of the wall (15) or the absence of the wall (15) in the environment of the enclosure (10) by applying a neural network model (70) to the spectrogram of the directions of arrival and the determined energy levels.

10. Computer program product comprising software instructions which, when executed by a computer, implement a detection method according to the preceding claim.

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