blob: e0bb8685aa02ba6961af66a842a9a15345fb6cc4 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
|
/*
* Copyright (c) 2021-2022 Arm Limited. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef KWS_RESULT_HPP
#define KWS_RESULT_HPP
#include "ClassificationResult.hpp"
#include <vector>
namespace arm {
namespace app {
namespace kws {
using ResultVec = std::vector<arm::app::ClassificationResult>;
/* Structure for holding kws result. */
class KwsResult {
public:
ResultVec m_resultVec; /* Container for "thresholded" classification results. */
float m_timeStamp; /* Audio timestamp for this result. */
uint32_t m_inferenceNumber; /* Corresponding inference number. */
float m_threshold; /* Threshold value for `m_resultVec`. */
KwsResult() = delete;
KwsResult(ResultVec& resultVec,
const float timestamp,
const uint32_t inferenceIdx,
const float scoreThreshold) {
this->m_threshold = scoreThreshold;
this->m_timeStamp = timestamp;
this->m_inferenceNumber = inferenceIdx;
this->m_resultVec = ResultVec();
for (auto& i : resultVec) {
if (i.m_normalisedVal >= this->m_threshold) {
this->m_resultVec.emplace_back(i);
}
}
}
~KwsResult() = default;
};
} /* namespace kws */
} /* namespace app */
} /* namespace arm */
#endif /* KWS_RESULT_HPP */
|
